estimating_rbc.ipynb

{
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   "source": [
    "# Estimating a Real Business Cycle DSGE Model by Maximum Likelihood in Python\n",
    "\n",
    "This notebook demonstrates how to setup, solve, and estimate a simple real business cycle model in Python. The model is very standard; the setup and notation here is a hybrid of Ruge-Murcia (2007) and DeJong and Dave (2011).\n",
    "\n",
    "Since we will be proceeding step-by-step, the code will match that progression by generating a series of child classes, so that we can add the functionality step-by-step. Of course, in practice a single class incorporating all the functionality would be all you would need."
   ]
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   "source": [
    "%matplotlib inline\n",
    "from __future__ import division\n",
    "\n",
    "import numpy as np\n",
    "from scipy import optimize, signal\n",
    "import pandas as pd\n",
    "from pandas_datareader.data import DataReader\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tools.numdiff import approx_fprime, approx_fprime_cs\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "\n",
    "from numpy.testing import assert_allclose\n",
    "\n",
    "# Set some pretty-printing options\n",
    "np.set_printoptions(precision=3, suppress=True, linewidth=120)\n",
    "pd.set_option('float_format', lambda x: '%.3g' % x, )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Household problem\n",
    "\n",
    "$$\n",
    "\\max E_0 \\sum_{t=0}^\\infty \\beta^t u(c_t, l_t)\n",
    "$$\n",
    "\n",
    "subject to:\n",
    "\n",
    "- the budget constraint: $y_t = c_t + i_t$\n",
    "- the capital accumulation equation: $k_{t+1} = (1 - \\delta) k_t + i_t$\n",
    "- $1 = l_t + n_t$\n",
    "\n",
    "where households have the following production technology:\n",
    "\n",
    "$$y_t = z_t f(k_t, n_t)$$\n",
    "\n",
    "and where the (log of the) technology process follows an AR(1) process:\n",
    "\n",
    "$$\n",
    "\\log z_t = \\rho \\log z_{t-1} + \\varepsilon_t, \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)\n",
    "$$\n",
    "\n",
    "## Functional forms\n",
    "\n",
    "We will assume additively separable utility in consumption and leisure, with:\n",
    "\n",
    "$$u(c_t, n_t) = \\log(c_t) + \\psi l_t $$\n",
    "\n",
    "where $\\psi$ is a scalar weight.\n",
    "\n",
    "The production function will be Cobb-Douglas:\n",
    "\n",
    "$$f(k_t, n_t) = k_t^\\alpha n_t^{1 - \\alpha}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimal behavior\n",
    "\n",
    "Optimal household behavior is standard:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\psi & = \\frac{1}{c_t} (1 - \\alpha) z_t \\left ( \\frac{k_t}{n_t} \\right )^{\\alpha} \\\\\n",
    "\\frac{1}{c_t} & = \\beta E_t \\left \\{ \\frac{1}{c_{t+1}} \\left [ \\alpha z_{t+1} \\left ( \\frac{k_{t+1}}{n_{t+1}} \\right )^{\\alpha - 1} + (1 - \\delta) \\right ] \\right \\} \\\\\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nonlinear system\n",
    "\n",
    "Collecting all of the model equations, we have:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\psi c_t & = (1 - \\alpha) z_t \\left ( \\frac{k_t}{n_t} \\right )^{\\alpha} & \\text{Static FOC} \\\\\n",
    "\\frac{1}{c_t} & = \\beta E_t \\left \\{ \\frac{1}{c_{t+1}} \\left [ \\alpha z_{t+1} \\left ( \\frac{k_{t+1}}{n_{t+1}} \\right )^{\\alpha - 1} + (1 - \\delta) \\right ] \\right \\} & \\text{Euler equation} \\\\\n",
    "y_t & = z_t k_t^\\alpha n_t^{1 - \\alpha} & \\text{Production function} \\\\\n",
    "y_t & = c_t + i_t & \\text{Aggregate resource constraint} \\\\\n",
    "k_{t+1} & = (1 - \\delta) k_t + i_t & \\text{Captial accumulation} \\\\\n",
    "1 & = l_t + n_t & \\text{Labor-leisure tradeoff} \\\\\n",
    "\\log z_t & = \\rho \\log z_{t-1} + \\varepsilon_t & \\text{Technology shock transition}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "### Variables / Parameters\n",
    "\n",
    "This system is in the following variables:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "x_t = \\{ \\quad & &\\\\\n",
    "& y_t, & \\text{Output}\\\\\n",
    "& c_t, & \\text{Consumption}\\\\\n",
    "& i_t, & \\text{Investment}\\\\\n",
    "& n_t, & \\text{Labor}\\\\\n",
    "& l_t, & \\text{Leisure}\\\\\n",
    "& k_t, & \\text{Capital}\\\\\n",
    "& z_t  & \\text{Technology} \\\\\n",
    "\\} \\quad & &\n",
    "\\end{align}$$\n",
    "\n",
    "and depends on the following parameters:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\{ \\quad & &\\\\\n",
    "& \\beta, & \\text{Discount rate}\\\\\n",
    "& \\psi, & \\text{Marginal disutility of labor}\\\\\n",
    "& \\delta, & \\text{Depreciation rate}\\\\\n",
    "& \\alpha, & \\text{Capital-share of output}\\\\\n",
    "& \\rho, & \\text{Technology shock persistence}\\\\\n",
    "& \\sigma^2 & \\text{Technology shock variance}\\\\\n",
    "\\} \\quad & &\n",
    "\\end{align}$$"
   ]
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   "source": [
    "# Save the names of the equations, variables, and parameters\n",
    "equation_names = [\n",
    "    'static FOC', 'euler equation', 'production',\n",
    "    'aggregate resource constraint', 'capital accumulation',\n",
    "    'labor-leisure', 'technology shock transition'\n",
    "]\n",
    "variable_names = [\n",
    "    'output', 'consumption', 'investment',\n",
    "    'labor', 'leisure', 'capital', 'technology'\n",
    "]\n",
    "parameter_names = [\n",
    "    'discount rate', 'marginal disutility of labor',\n",
    "    'depreciation rate', 'capital share',\n",
    "    'technology shock persistence',\n",
    "    'technology shock standard deviation',\n",
    "]\n",
    "\n",
    "# Save some symbolic forms for pretty-printing\n",
    "variable_symbols = [\n",
    "    r\"y\", r\"c\", r\"i\", r\"n\", r\"l\", r\"k\", r\"z\",\n",
    "]\n",
    "contemporaneous_variable_symbols = [\n",
    "    r\"$%s_t$\" % symbol for symbol in variable_symbols\n",
    "]\n",
    "lead_variable_symbols = [\n",
    "    r\"$%s_{t+1}$\" % symbol for symbol in variable_symbols\n",
    "]\n",
    "\n",
    "parameter_symbols = [\n",
    "    r\"$\\beta$\", r\"$\\psi$\", r\"$\\delta$\", r\"$\\alpha$\", r\"$\\rho$\", r\"$\\sigma^2$\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical method\n",
    "\n",
    "Using these equations, we can numerically find the steady-state using a root-finding algorithm and can then log-linearize around the steady-state using a numerical gradient procedure. In particular, here we follow DeJong and Dave (2011) chapter 2 and 3.1.\n",
    "\n",
    "Write the generic non-linear system as:\n",
    "\n",
    "$$\n",
    "\\Gamma(E_t x_{t+1}, x_t, z_t) = 0\n",
    "$$\n",
    "\n",
    "in the absense of shocks, this can be rewritten as:\n",
    "\n",
    "$$\n",
    "\\Psi(x_{t+1}, x_t) = 0\n",
    "$$\n",
    "\n",
    "or as:\n",
    "\n",
    "$$\n",
    "\\Psi_1(x_{t+1}, x_t) = \\Psi_2(x_{t+1}, x_t)\n",
    "$$\n",
    "\n",
    "and finally in logs as\n",
    "\n",
    "$$\n",
    "\\log \\Psi_1(e^{\\log x_{t+1}}, e^{\\log x_t}) - \\log \\Psi_2(e^{\\log x_{t+1}}, e^{\\log x_t}) = 0\n",
    "$$\n",
    "\n",
    "First, we define a new class (`RBC1`) which holds the state of the RBC model (its dimensions, parameters, etc) and has methods for evaluating the log system. The `eval_logged` method evaluates the last equation, above.\n",
    "\n",
    "Notice that the order of variables and order of equations is as described above."
   ]
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   "source": [
    "class RBC1(object):\n",
    "    def __init__(self, params=None):\n",
    "        # Model dimensions\n",
    "        self.k_params = 6\n",
    "        self.k_variables = 7\n",
    "        \n",
    "        # Initialize parameters\n",
    "        if params is not None:\n",
    "            self.update(params)\n",
    "    \n",
    "    def update(self, params):\n",
    "        # Save deep parameters\n",
    "        self.discount_rate = params[0]\n",
    "        self.disutility_labor = params[1]\n",
    "        self.depreciation_rate = params[2]\n",
    "        self.capital_share = params[3]\n",
    "        self.technology_shock_persistence = params[4]\n",
    "        self.technology_shock_std = params[5]\n",
    "        \n",
    "    def eval_logged(self, log_lead, log_contemporaneous):\n",
    "        (log_lead_output, log_lead_consumption, log_lead_investment,\n",
    "         log_lead_labor, log_lead_leisure, log_lead_capital,\n",
    "         log_lead_technology_shock) = log_lead\n",
    "        \n",
    "        (log_output, log_consumption, log_investment, log_labor,\n",
    "         log_leisure, log_capital, log_technology_shock) = log_contemporaneous\n",
    "        \n",
    "        return np.r_[\n",
    "            self.log_static_foc(\n",
    "                log_lead_consumption, log_lead_labor,\n",
    "                log_lead_capital, log_lead_technology_shock\n",
    "            ),\n",
    "            self.log_euler_equation(\n",
    "                log_lead_consumption, log_lead_labor,\n",
    "                log_lead_capital, log_lead_technology_shock,\n",
    "                log_consumption\n",
    "            ),\n",
    "            self.log_production(\n",
    "                log_lead_output, log_lead_labor, log_lead_capital,\n",
    "                log_lead_technology_shock\n",
    "            ),\n",
    "            self.log_aggregate_resource_constraint(\n",
    "                log_lead_output, log_lead_consumption,\n",
    "                log_lead_investment\n",
    "            ),\n",
    "            self.log_capital_accumulation(\n",
    "                log_lead_capital, log_investment, log_capital\n",
    "            ),\n",
    "            self.log_labor_leisure_constraint(\n",
    "                log_lead_labor, log_lead_leisure\n",
    "            ),\n",
    "            self.log_technology_shock_transition(\n",
    "                log_lead_technology_shock, log_technology_shock\n",
    "            )\n",
    "        ]\n",
    "    \n",
    "    def log_static_foc(self, log_lead_consumption, log_lead_labor,\n",
    "                       log_lead_capital, log_lead_technology_shock):\n",
    "        return (\n",
    "            np.log(self.disutility_labor) +\n",
    "            log_lead_consumption -\n",
    "            np.log(1 - self.capital_share) -\n",
    "            log_lead_technology_shock -\n",
    "            self.capital_share * (log_lead_capital - log_lead_labor)\n",
    "        )\n",
    "        \n",
    "    def log_euler_equation(self, log_lead_consumption, log_lead_labor,\n",
    "                           log_lead_capital, log_lead_technology_shock,\n",
    "                           log_consumption):\n",
    "        return (\n",
    "            -log_consumption -\n",
    "            np.log(self.discount_rate) +\n",
    "            log_lead_consumption -\n",
    "            np.log(\n",
    "                (self.capital_share *\n",
    "                 np.exp(log_lead_technology_shock) * \n",
    "                 np.exp((1 - self.capital_share) * log_lead_labor) /\n",
    "                 np.exp((1 - self.capital_share) * log_lead_capital)) +\n",
    "                (1 - self.depreciation_rate)\n",
    "            )\n",
    "        )\n",
    "        \n",
    "    def log_production(self, log_lead_output, log_lead_labor, log_lead_capital,\n",
    "                       log_lead_technology_shock):\n",
    "        return (\n",
    "            log_lead_output -\n",
    "            log_lead_technology_shock -\n",
    "            self.capital_share * log_lead_capital -\n",
    "            (1 - self.capital_share) * log_lead_labor\n",
    "        )\n",
    "        \n",
    "    def log_aggregate_resource_constraint(self, log_lead_output, log_lead_consumption,\n",
    "                                          log_lead_investment):\n",
    "        return (\n",
    "            log_lead_output -\n",
    "            np.log(np.exp(log_lead_consumption) + np.exp(log_lead_investment))\n",
    "        )\n",
    "    \n",
    "    def log_capital_accumulation(self, log_lead_capital, log_investment, log_capital):\n",
    "        return (\n",
    "            log_lead_capital -\n",
    "            np.log(np.exp(log_investment) + (1 - self.depreciation_rate) * np.exp(log_capital))\n",
    "        )\n",
    "    \n",
    "    def log_labor_leisure_constraint(self, log_lead_labor, log_lead_leisure):\n",
    "        return (\n",
    "            -np.log(np.exp(log_lead_labor) + np.exp(log_lead_leisure))\n",
    "        )\n",
    "    \n",
    "    def log_technology_shock_transition(self, log_lead_technology_shock, log_technology_shock):\n",
    "        return (\n",
    "            log_lead_technology_shock -\n",
    "            self.technology_shock_persistence * log_technology_shock\n",
    "        )"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Later we will estimate (some of) the parameters; in the meantime we fix them at the values used to generate the datasets in Ruge-Murcia (2007)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>discount rate</td>\n",
       "      <td>marginal disutility of labor</td>\n",
       "      <td>depreciation rate</td>\n",
       "      <td>capital share</td>\n",
       "      <td>technology shock persistence</td>\n",
       "      <td>technology shock standard deviation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>0.95</td>\n",
       "      <td>3</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.04</td>\n",
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      "text/plain": [
       "                   0                             1                  2  \\\n",
       "name   discount rate  marginal disutility of labor  depreciation rate   \n",
       "value           0.95                             3              0.025   \n",
       "\n",
       "                   3                             4  \\\n",
       "name   capital share  technology shock persistence   \n",
       "value           0.36                          0.85   \n",
       "\n",
       "                                         5  \n",
       "name   technology shock standard deviation  \n",
       "value                                 0.04  "
      ]
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     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# Setup fixed parameters\n",
    "parameters = pd.DataFrame({\n",
    "    'name': parameter_names,\n",
    "    'value': [0.95, 3, 0.025, 0.36, 0.85, 0.04]\n",
    "})\n",
    "parameters.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steady state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numeric calculation\n",
    "\n",
    "To numerically calculate steady-state, we apply a root-finding algorithm to the `eval_logged` method. In particular, we are finding values $\\bar x$ such that\n",
    "\n",
    "$$\\log \\Psi_1(e^{\\log \\bar x}, e^{\\log \\bar x}) - \\log \\Psi_2(e^{\\log \\bar x}, e^{\\log \\bar x}) = 0$$\n",
    "\n",
    "These will be confirmed analytically, below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a derived class, `RBC2` which extends all of the functionality from above, but now includes methods for numerical calcualtion of the steady-state."
   ]
  },
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   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>output</th>\n",
       "      <th>consumption</th>\n",
       "      <th>investment</th>\n",
       "      <th>labor</th>\n",
       "      <th>leisure</th>\n",
       "      <th>capital</th>\n",
       "      <th>technology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>0.572</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.0663</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.759</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "       output  consumption  investment  labor  leisure  capital  technology\n",
       "value   0.572        0.506      0.0663  0.241    0.759     2.65           1"
      ]
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   "source": [
    "class RBC2(RBC1):\n",
    "    def steady_state_numeric(self):\n",
    "        # Setup starting parameters\n",
    "        log_start_vars = [0.5] * self.k_variables  # very arbitrary\n",
    "\n",
    "        # Setup the function the evaluate\n",
    "        eval_logged = lambda log_vars: self.eval_logged(log_vars, log_vars)\n",
    "\n",
    "        # Apply the root-finding algorithm\n",
    "        result = optimize.root(eval_logged, log_start_vars)\n",
    "        \n",
    "        return np.exp(result.x)\n",
    "\n",
    "mod2 = RBC2(parameters['value'])\n",
    "\n",
    "steady_state = pd.DataFrame({\n",
    "    'value': mod2.steady_state_numeric()\n",
    "}, index=variable_names)\n",
    "\n",
    "steady_state.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analytic evaluation\n",
    "\n",
    "In this case, we can analytically evaluate the steady-state:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\frac{\\bar y}{\\bar n} & = \\eta \\\\\n",
    "\\frac{\\bar c}{\\bar n} & = \\eta - \\delta \\theta \\\\\n",
    "\\frac{\\bar i}{\\bar n} & = \\delta \\theta \\\\\n",
    "\\bar n & = \\frac{1}{\\left ( \\frac{1}{1 - \\alpha} \\right ) \\psi \\left [ 1 - \\delta \\theta^{1-\\alpha} \\right ]} \\\\\n",
    "\\bar l & = 1 - \\bar n \\\\\n",
    "\\frac{\\bar k}{\\bar n} & = \\theta\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where\n",
    "$$\n",
    "\\begin{align}\n",
    "\\theta & = \\left ( \\frac{\\alpha}{1/\\beta - (1 - \\delta)} \\right )^\\frac{1}{1-\\alpha} \\\\\n",
    "\\eta & = \\theta^\\alpha\n",
    "\\end{align}\n",
    "$$"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>output</th>\n",
       "      <th>consumption</th>\n",
       "      <th>investment</th>\n",
       "      <th>labor</th>\n",
       "      <th>leisure</th>\n",
       "      <th>capital</th>\n",
       "      <th>technology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>analytic</th>\n",
       "      <td>0.572</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.0663</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.759</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numeric</th>\n",
       "      <td>0.572</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.0663</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.759</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          output  consumption  investment  labor  leisure  capital  technology\n",
       "analytic   0.572        0.506      0.0663  0.241    0.759     2.65           1\n",
       "numeric    0.572        0.506      0.0663  0.241    0.759     2.65           1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class RBC3(RBC2):\n",
    "    \n",
    "    def update(self, params):\n",
    "        # Update the deep parameters\n",
    "        super(RBC3, self).update(params)\n",
    "        \n",
    "        # And now also calculate some intermediate parameters\n",
    "        self.theta = (self.capital_share / (\n",
    "            1 / self.discount_rate -\n",
    "            (1 - self.depreciation_rate)\n",
    "        ))**(1 / (1 - self.capital_share))\n",
    "        \n",
    "        self.eta = self.theta**self.capital_share\n",
    "    \n",
    "    def steady_state_analytic(self):\n",
    "        steady_state = np.zeros(7)\n",
    "\n",
    "        # Labor (must be computed first)\n",
    "        numer = (1 - self.capital_share) / self.disutility_labor\n",
    "        denom = (1 - self.depreciation_rate * self.theta**(1 - self.capital_share))\n",
    "        steady_state[3] = numer / denom\n",
    "        # Output\n",
    "        steady_state[0] = self.eta * steady_state[3]\n",
    "        # Consumption\n",
    "        steady_state[1] = (1 - self.capital_share) * self.eta / self.disutility_labor\n",
    "        # Investment\n",
    "        steady_state[2] = self.depreciation_rate * self.theta * steady_state[3]\n",
    "        # Labor (computed already)\n",
    "        # Leisure\n",
    "        steady_state[4] = 1 - steady_state[3]\n",
    "        # Capital\n",
    "        steady_state[5] = self.theta * steady_state[3]\n",
    "        # Technology shock\n",
    "        steady_state[6] = 1\n",
    "        \n",
    "        return steady_state\n",
    "    \n",
    "mod3 = RBC3(parameters['value'])\n",
    "\n",
    "steady_state = pd.DataFrame({\n",
    "    'numeric': mod3.steady_state_numeric(),\n",
    "    'analytic': mod3.steady_state_analytic()\n",
    "}, index=variable_names)\n",
    "\n",
    "steady_state.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log-linearization\n",
    "\n",
    "The system we wrote down, above, was non-linear. In order to estimate it, we want to get it in a linear form:\n",
    "\n",
    "$$\n",
    "A E_t \\tilde x_{t+1} = B \\tilde x_{t} + C v_{t+1}\n",
    "$$\n",
    "\n",
    "where $v_{t+1}$ contains structural shocks (here, $z_t$ is included in the $x_t$ vector, and the only structural shock is $\\varepsilon_t$, the innovation to $z_t$).\n",
    "\n",
    "This can be achieved via log-linearization around the steady state. In this case, DeJong and Dave (2011) show that:\n",
    "\n",
    "$$\n",
    "A = \\left [ \\frac{\\partial \\log [ \\Psi_1 ]}{\\partial \\log(x_{t+1})} (\\bar x) - \\frac{\\partial \\log [ \\Psi_2 ]}{\\partial \\log(x_{t+1})} (\\bar x) \\right ] \\\\\n",
    "B = - \\left [ \\frac{\\partial \\log [ \\Psi_1 ]}{\\partial \\log(x_t)} (\\bar x) - \\frac{\\partial \\log [ \\Psi_2 ]}{\\partial \\log(x_t)} (\\bar x) \\right ] \\\\\n",
    "$$\n",
    "\n",
    "where $\\tilde x_t = \\log \\left ( \\frac{x_t}{\\bar x} \\right )$ expresses the variables in proportional deviation from steady-state form.\n",
    "\n",
    "The matrix $C$ can be constructed by observation. In this case, we have:\n",
    "\n",
    "$$\n",
    "C = \\begin{bmatrix} 0 & 0 & 0 & 0 & 0 & 0 & 1 \\end{bmatrix} \\qquad \\text{and} \\qquad v_{t+1} \\equiv \\varepsilon_t\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numeric calculation\n",
    "\n",
    "Since the `eval_logged` method of our class evaluates $\\log \\Psi_1(e^{\\log x_{t+1}}, e^{\\log x_t}) - \\log \\Psi_2(e^{\\log x_{t+1}}, e^{\\log x_t})$, we can apply a numerical gradient procedure to it to get $A$, when we differentiate with respect to the lead variables, and $B$, when we differentiate with respect to the contemporaneous variables."
   ]
  },
  {
   "cell_type": "code",
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       "      <th>static FOC</th>\n",
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       "      <td>0</td>\n",
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       "      <th>labor-leisure</th>\n",
       "      <td>-0</td>\n",
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       "      <td>-0</td>\n",
       "      <td>-0.241</td>\n",
       "      <td>-0.759</td>\n",
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       "                               $y_{t+1}$  $c_{t+1}$  $i_{t+1}$  $n_{t+1}$  \\\n",
       "static FOC                             0          1          0       0.36   \n",
       "euler equation                         0          1          0    -0.0472   \n",
       "production                             1          0          0      -0.64   \n",
       "aggregate resource constraint          1     -0.884     -0.116          0   \n",
       "capital accumulation                   0          0          0          0   \n",
       "labor-leisure                         -0         -0         -0     -0.241   \n",
       "technology shock transition            0          0          0          0   \n",
       "\n",
       "                               $l_{t+1}$  $k_{t+1}$  $z_{t+1}$  \n",
       "static FOC                             0      -0.36         -1  \n",
       "euler equation                         0     0.0472    -0.0737  \n",
       "production                             0      -0.36         -1  \n",
       "aggregate resource constraint          0          0          0  \n",
       "capital accumulation                   0          1          0  \n",
       "labor-leisure                     -0.759         -0         -0  \n",
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       "                               $y_t$  $c_t$  $i_t$  $n_t$  $l_t$  $k_t$  $z_t$\n",
       "static FOC                        -0     -0     -0     -0     -0     -0     -0\n",
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       "production                        -0     -0     -0     -0     -0     -0     -0\n",
       "aggregate resource constraint     -0     -0     -0     -0     -0     -0     -0\n",
       "capital accumulation              -0     -0  0.025     -0     -0  0.975     -0\n",
       "labor-leisure                     -0     -0     -0     -0     -0     -0     -0\n",
       "technology shock transition       -0     -0     -0     -0     -0     -0   0.85"
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       "                               $\\varepsilon_t$\n",
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   "source": [
    "class RBC4(RBC3):\n",
    "    \n",
    "    def A_numeric(self):\n",
    "        log_steady_state = np.log(self.steady_state_analytic())\n",
    "\n",
    "        eval_logged_lead = lambda log_lead: self.eval_logged(log_lead, log_steady_state)\n",
    "        \n",
    "        return approx_fprime_cs(log_steady_state, eval_logged_lead)\n",
    "\n",
    "    def B_numeric(self):\n",
    "        log_steady_state = np.log(self.steady_state_analytic())\n",
    "        \n",
    "        eval_logged_contemporaneous = lambda log_contemp: self.eval_logged(log_steady_state, log_contemp)\n",
    "        \n",
    "        return -approx_fprime_cs(log_steady_state, eval_logged_contemporaneous)\n",
    "    \n",
    "    def C(self):\n",
    "        return np.r_[[0]*(self.k_variables-1), 1]\n",
    "\n",
    "mod4 = RBC4(parameters['value'])\n",
    "        \n",
    "display(pd.DataFrame(mod4.A_numeric(), index=equation_names, columns=lead_variable_symbols))\n",
    "display(pd.DataFrame(mod4.B_numeric(), index=equation_names, columns=contemporaneous_variable_symbols))\n",
    "display(pd.DataFrame(mod4.C(), index=equation_names, columns=[r'$\\varepsilon_t$']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analytic evaluation\n",
    "\n",
    "This system can be log-linearized directly, as well, yielding (see e.g. Ruge-Murcia (2007) Appendix A for these formulas, with slightly different notation):\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\tilde c_t & = \\tilde z_t + \\alpha \\tilde k_t - \\alpha \\tilde n_t \\\\\n",
    "-\\tilde c_t & = -E_t \\tilde c_{t+1} + \\zeta (\\alpha - 1) E_t \\tilde k_{t+1} + \\zeta (1 - \\alpha) E_t \\tilde n_{t+1} + \\zeta E_t \\tilde z_{t+1} \\\\\n",
    "\\tilde y_t & = \\tilde z_t + \\alpha \\tilde k_t + (1 - \\alpha) \\tilde n_t \\\\\n",
    "\\tilde y_t & = \\gamma \\tilde c_t + (1 - \\gamma) \\tilde i_t \\\\\n",
    "\\tilde k_{t+1} & = (1 - \\delta) \\tilde k_t + \\delta \\tilde i_t \\\\\n",
    "0 & = \\frac{\\bar l}{\\bar l + \\bar n} \\tilde l_{t+1} + \\frac{\\bar n}{\\bar l + \\bar n} \\tilde n_{t+1} \\\\\n",
    "\\tilde z_{t+1} & = \\rho \\tilde z_t + \\varepsilon_t\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where $\\zeta = \\alpha \\beta \\theta^{\\alpha - 1}$ and $\\gamma = 1 - \\delta \\theta^{1 - \\alpha}$ is the steady-state consumption-output ratio."
   ]
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       "      <th>production</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aggregate resource constraint</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.884</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital accumulation</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor-leisure</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.241</td>\n",
       "      <td>-0.759</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technology shock transition</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               $y_{t+1}$  $c_{t+1}$  $i_{t+1}$  $n_{t+1}$  \\\n",
       "static FOC                             0          1          0       0.36   \n",
       "euler equation                         0          1          0    -0.0472   \n",
       "production                             1          0          0      -0.64   \n",
       "aggregate resource constraint          1     -0.884     -0.116          0   \n",
       "capital accumulation                   0          0          0          0   \n",
       "labor-leisure                          0          0          0     -0.241   \n",
       "technology shock transition            0          0          0          0   \n",
       "\n",
       "                               $l_{t+1}$  $k_{t+1}$  $z_{t+1}$  \n",
       "static FOC                             0      -0.36         -1  \n",
       "euler equation                         0     0.0472    -0.0737  \n",
       "production                             0      -0.36         -1  \n",
       "aggregate resource constraint          0          0          0  \n",
       "capital accumulation                   0          1          0  \n",
       "labor-leisure                     -0.759          0          0  \n",
       "technology shock transition            0          0          1  "
      ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$y_{t+1}$</th>\n",
       "      <th>$c_{t+1}$</th>\n",
       "      <th>$i_{t+1}$</th>\n",
       "      <th>$n_{t+1}$</th>\n",
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       "      <th>static FOC</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euler equation</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>production</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>aggregate resource constraint</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>capital accumulation</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labor-leisure</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technology shock transition</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
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       "</table>\n",
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       "                               $y_{t+1}$  $c_{t+1}$  $i_{t+1}$  $n_{t+1}$  \\\n",
       "static FOC                             0          0          0          0   \n",
       "euler equation                         0          1          0          0   \n",
       "production                             0          0          0          0   \n",
       "aggregate resource constraint          0          0          0          0   \n",
       "capital accumulation                   0          0      0.025          0   \n",
       "labor-leisure                          0          0          0          0   \n",
       "technology shock transition            0          0          0          0   \n",
       "\n",
       "                               $l_{t+1}$  $k_{t+1}$  $z_{t+1}$  \n",
       "static FOC                             0          0          0  \n",
       "euler equation                         0          0          0  \n",
       "production                             0          0          0  \n",
       "aggregate resource constraint          0          0          0  \n",
       "capital accumulation                   0      0.975          0  \n",
       "labor-leisure                          0          0          0  \n",
       "technology shock transition            0          0       0.85  "
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   "source": [
    "class RBC5(RBC4):\n",
    "    \n",
    "    def update(self, params):\n",
    "        super(RBC5, self).update(params)\n",
    "        \n",
    "        # Now calculate some more intermediate parameters\n",
    "        self.gamma = 1 - self.depreciation_rate * self.theta**(1 - self.capital_share)\n",
    "        self.zeta = self.capital_share * self.discount_rate * self.theta**(self.capital_share - 1)\n",
    "    \n",
    "    def A_analytic(self):\n",
    "        steady_state = self.steady_state_analytic()\n",
    "        \n",
    "        A = np.array([\n",
    "            [0, 1, 0, self.capital_share, 0, -self.capital_share, -1],\n",
    "            [0, 1, 0, self.zeta * (self.capital_share - 1), 0, self.zeta * (1 - self.capital_share), -self.zeta],\n",
    "            [1, 0, 0, (self.capital_share - 1), 0, -self.capital_share, -1],\n",
    "            [1, -self.gamma, (self.gamma - 1), 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 0, 1, 0],\n",
    "            [0, 0, 0, -steady_state[3], -steady_state[4], 0, 0],\n",
    "            [0, 0, 0, 0, 0, 0, 1],\n",
    "        ])\n",
    "        \n",
    "        return A\n",
    "\n",
    "    def B_analytic(self):\n",
    "        \n",
    "        B = np.array([\n",
    "            [0, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 1, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 0, self.depreciation_rate, 0, 0, 1 - self.depreciation_rate, 0],\n",
    "            [0, 0, 0, 0, 0, 0, 0],\n",
    "            [0, 0, 0, 0, 0, 0, self.technology_shock_persistence],\n",
    "        ])\n",
    "        \n",
    "        return B\n",
    "\n",
    "mod5 = RBC5(parameters['value'])\n",
    "\n",
    "display(pd.DataFrame(mod5.A_analytic(), index=equation_names, columns=lead_variable_symbols))\n",
    "assert(np.all(np.abs(mod5.A_numeric() - mod5.A_analytic()) < 1e-10))\n",
    "\n",
    "display(pd.DataFrame(mod5.B_analytic(), index=equation_names, columns=lead_variable_symbols))\n",
    "assert(np.all(np.abs(mod5.B_numeric() - mod5.B_analytic()) < 1e-10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## System reduction\n",
    "\n",
    "The system currently has 7 equations in 7 unknowns. This can be reduced into a system of 3 equations in 3 unknowns (consumption, capital, and the technology shock), by substituting out output, investment, labor, and leisure. Given a solution to the reduced system, the remaining four unknowns can be calculated. Also, we remove the technology shock transition from the state vector and include it instead in the stochastic shocks component $(v_{t+1})$.\n",
    "\n",
    "First, notice that (using the static first order condition, production function, aggregate resource constraint, and labor-leisure tradeoff equations, respectively):\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\tilde n_t & = - \\frac{1}{\\alpha} \\tilde c_t + \\frac{1}{\\alpha} \\tilde z_t + \\tilde k_t \\\\\n",
    "\\tilde y_t & = \\tilde z_t + \\alpha \\tilde k_t + (1 - \\alpha) \\left [ - \\frac{1}{\\alpha} \\tilde c_t + \\frac{1}{\\alpha} \\tilde z_t + \\tilde k_t \\right ] \\\\\n",
    "& = \\frac{1}{\\alpha} \\tilde z_t - \\frac{1 - \\alpha}{\\alpha} \\tilde c_t + \\tilde k_t \\\\\n",
    "\\tilde i_t & = \\frac{1}{1 - \\gamma} \\left [ \\frac{1}{\\alpha} \\tilde z_t - \\frac{1 - \\alpha}{\\alpha} \\tilde c_t + \\tilde k_t - \\gamma \\tilde c_t \\right ] \\\\\n",
    "& = \\frac{1}{1 - \\gamma} \\left [ \\frac{1}{\\alpha} \\tilde z_t - \\frac{1 - \\alpha + \\gamma \\alpha}{\\alpha} \\tilde c_t + \\tilde k_t \\right ] \\\\\n",
    "\\tilde l_t & = - \\frac{\\bar n}{1 - \\bar n} \\tilde n_t\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "then we can plug these values into the remaining three equations (Euler, capital accumulation, and shock transition equations):\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "k_{t+1} & = (1 - \\delta) \\tilde k_t + \\delta \\frac{1}{1 - \\gamma} \\left [ \\frac{1}{\\alpha} \\tilde z_t - \\frac{1 - \\alpha + \\gamma \\alpha}{\\alpha} \\tilde c_t + \\tilde k_t \\right ] \\\\\n",
    "& = \\underbrace{\\left [ 1 + \\delta \\left ( \\frac{\\gamma}{1 - \\gamma} \\right ) \\right ]}_{B_{11}, F_{11}} \\tilde k_t + \\underbrace{\\frac{-\\delta (1 - \\alpha + \\gamma \\alpha)}{\\alpha (1 - \\gamma)}}_{B_{12}, F_{12}} \\tilde c_t + \\underbrace{\\frac{\\delta}{\\alpha (1 - \\gamma)}}_{C_1, G_{12}} \\tilde z_t\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "and\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "E_t \\tilde c_{t+1} & = \\tilde c_t + \\zeta (\\alpha - 1) E_t k_{t+1} + \\zeta (1 - \\alpha) E_t \\left [ - \\frac{1}{\\alpha} \\tilde c_{t+1} + \\frac{1}{\\alpha} \\tilde z_{t+1} + \\tilde k_{t+1} \\right ] + \\zeta E_t \\tilde z_{t+1} \\\\\n",
    "& = \\tilde c_t + \\zeta (\\alpha - 1) E_t \\tilde k_{t+1} - \\zeta \\frac{(1 - \\alpha)}{\\alpha} E_t \\tilde c_{t+1} + \\zeta \\frac{(1 - \\alpha)}{\\alpha} E_t z_{t+1} + \\zeta (1 - \\alpha) E_t \\tilde k_{t+1} + \\zeta E_t \\tilde z_{t+1} \\\\\n",
    "& = \\underbrace{\\frac{\\alpha}{\\zeta + \\alpha (1 - \\zeta)}}_{B_{22}} \\tilde c_t + \\underbrace{\\frac{\\zeta \\rho}{\\zeta + \\alpha (1 - \\zeta )}}_{C_2} \\tilde z_t\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "and\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\tilde z_{t+1} & = \\rho \\tilde z_t + \\varepsilon_t\n",
    "\\end{align}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save the names of the equations, variables, and parameters\n",
    "# in the reduced model\n",
    "reduced_equation_names = [\n",
    "    'euler equation', 'capital accumulation'\n",
    "]\n",
    "reduced_variable_names = [\n",
    "    'consumption', 'capital'\n",
    "]\n",
    "reduced_parameter_names = parameter_names\n",
    "\n",
    "# Save some symbolic forms for pretty-printing\n",
    "reduced_variable_symbols = [\n",
    "    r\"c\", r\"k\"\n",
    "]\n",
    "reduced_contemporaneous_variable_symbols = [\n",
    "    r\"$%s_t$\" % symbol for symbol in reduced_variable_symbols\n",
    "]\n",
    "reduced_lead_variable_symbols = [\n",
    "    r\"$%s_{t+1}$\" % symbol for symbol in reduced_variable_symbols\n",
    "]\n",
    "\n",
    "reduced_parameter_symbols = parameter_symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>steady state</th>\n",
       "      <td>0.506</td>\n",
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       "              consumption  capital\n",
       "steady state        0.506     2.65"
      ]
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   "source": [
    "class ReducedRBC1(RBC5):\n",
    "    def __init__(self, params=None):\n",
    "        # Model dimensions\n",
    "        self.k_params = 6\n",
    "        self.k_variables = 2\n",
    "        \n",
    "        # Index for the reduced form variables\n",
    "        self.reduced_idx = [1, -2]\n",
    "        \n",
    "        # Initialize parameters\n",
    "        if params is not None:\n",
    "            self.update(params)\n",
    "\n",
    "    def steady_state_numeric(self):\n",
    "        return super(ReducedRBC1, self).steady_state_numeric()[self.reduced_idx]\n",
    "        \n",
    "    def steady_state_analytic(self):\n",
    "        return super(ReducedRBC1, self).steady_state_analytic()[self.reduced_idx]\n",
    "    \n",
    "    def A(self):\n",
    "        return np.eye(self.k_variables)\n",
    "    \n",
    "    def B(self):\n",
    "        B11 = 1 + self.depreciation_rate * (self.gamma / (1 - self.gamma))\n",
    "        B12 = (\n",
    "            -self.depreciation_rate *\n",
    "            (1 - self.capital_share + self.gamma * self.capital_share) /\n",
    "            (self.capital_share * (1 - self.gamma))\n",
    "        )\n",
    "        B21 = 0\n",
    "        B22 = self.capital_share / (self.zeta + self.capital_share*(1 - self.zeta))\n",
    "        \n",
    "        return np.array([[B11, B12],\n",
    "                         [B21, B22]])\n",
    "        \n",
    "    def C(self):\n",
    "        C1 = self.depreciation_rate / (self.capital_share * (1 - self.gamma))\n",
    "        C2 = (\n",
    "            self.zeta * self.technology_shock_persistence /\n",
    "            (self.zeta + self.capital_share*(1 - self.zeta))\n",
    "        )\n",
    "        return np.array([C1, C2])[:,np.newaxis]\n",
    "        \n",
    "# Setup the model\n",
    "reduced_mod1 = ReducedRBC1(parameters['value'])\n",
    "\n",
    "# Display the steady-states\n",
    "reduced_steady_state = pd.DataFrame({\n",
    "    'steady state': reduced_mod1.steady_state_analytic()\n",
    "}, index=reduced_variable_names)\n",
    "display(reduced_steady_state.T)\n",
    "\n",
    "# Test the model coefficients for accuracy\n",
    "# (results from \"Emsm\", downloaded from\n",
    "# https://sites.google.com/site/frugemurcia/home/replication-files\n",
    "# on 06/19/2015)\n",
    "assert_allclose(\n",
    "    reduced_mod1.B(),\n",
    "    np.array([[1.1906433, -0.57400910],\n",
    "              [0,          0.88408644]])\n",
    ")\n",
    "assert_allclose(\n",
    "    reduced_mod1.C(),\n",
    "    np.array([[0.59900910], [0.15394769]])\n",
    ")\n",
    "\n",
    "# Display the linear model coefficients\n",
    "display(pd.DataFrame(reduced_mod1.A(), index=reduced_equation_names, columns=reduced_lead_variable_symbols))\n",
    "display(pd.DataFrame(reduced_mod1.B(), index=reduced_equation_names, columns=reduced_contemporaneous_variable_symbols))\n",
    "display(pd.DataFrame(reduced_mod1.C(), index=reduced_equation_names, columns=[r'$z_t$']))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving the model\n",
    "\n",
    "This model can be solved using the method of Blanchard-Kahn (1980). First we put it in the required form:\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "\\tilde k_{t+1} \\\\\n",
    "E_t \\tilde c_{t+1}\n",
    "\\end{bmatrix} = \\begin{bmatrix}\n",
    "B_{11} & B_{12} \\\\\n",
    "B_{21} & B_{22}\n",
    "\\end{bmatrix} \\begin{bmatrix}\n",
    "\\tilde k_{t} \\\\\n",
    "\\tilde c_{t} \\\\\n",
    "\\end{bmatrix} + \\begin{bmatrix}\n",
    "C_{1} \\\\\n",
    "C_{2}\n",
    "\\end{bmatrix} \\tilde z_t\n",
    "$$\n",
    "\n",
    "and the solution yields the following equilibrium dynamics:\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "\\tilde k_{t+1} \\\\\n",
    "\\tilde c_{t+1}\n",
    "\\end{bmatrix} = \\begin{bmatrix}\n",
    "F_{11} & F_{12} \\\\\n",
    "F_{21} & F_{22}\n",
    "\\end{bmatrix} \\begin{bmatrix}\n",
    "\\tilde k_{t} \\\\\n",
    "\\tilde c_{t}\n",
    "\\end{bmatrix} + \\begin{bmatrix}\n",
    "G_{11} & G_{12} \\\\\n",
    "G_{21} & G_{22}\n",
    "\\end{bmatrix} \\begin{bmatrix}\n",
    "\\tilde z_{t+1} \\\\\n",
    "\\tilde z_t\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "in particular, we will find a policy function for the control variable, where the optimal choice for $\\tilde c_t$ depends only on the contemporaneous state variable $\\tilde k_t$ and exogenous shock $\\tilde z_t$. Then the policy expression can be substituted in to the reduced form capital accumulation equation, providing the evolution of the state variable in terms only of itself and the exogenous shock. The evolution of the exogenous shock is an $AR(1)$ as described above.\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\tilde c_t & = \\phi_{ck} \\tilde k_t + \\phi_{cz} \\tilde z_t \\\\\n",
    "\\tilde k_{t+1} & = \\underbrace{\\left [ F_{11} + F_{12} \\phi_{ck} \\right ]}_{T_{kk}} \\tilde k_t + \\underbrace{\\left [G_{12} + F_{12} \\phi_{cz} \\right ]}_{T_{kz}} \\tilde z_t \\\\\n",
    "\\tilde z_{t+1} & = \\rho \\tilde z_t + \\varepsilon_t \\\\\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where $F_{11}, F_{12},$ and $G_{12}$ are defined above (and we have $G_{11} = 0$), and $\\phi_{ck}, \\phi_{cz}$ are generated by the solution method. $F_{21}, F_{22}, G_{21}, G_{22}$ will not be required for estimation, but can be found by forwarding the equation for $c_t$ and plugging in for $k_{t+1}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some solution details\n",
    "\n",
    "Write the Jordan decomposition of $\\mathbf{B}$ as:\n",
    "\n",
    "$$\\mathbf{B} = \\Lambda^{-1} J \\Lambda$$\n",
    "\n",
    "where\n",
    "\n",
    "$$\n",
    "\\Lambda = \\begin{bmatrix}\n",
    "\\Lambda_{11} & \\Lambda_{12} \\\\\n",
    "\\Lambda_{21} & \\Lambda_{22} \\\\\n",
    "\\end{bmatrix}\n",
    "\\qquad \n",
    "J = \\begin{bmatrix}\n",
    "J_1 & 0 \\\\\n",
    "0 & J_2 \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "and we assume that the eigenvalues have been re-ordered so that the explosive ones are contained in $J_2$. Note that to solve the system using the Blanchard-Kahn method, we need to separate the variables into predetermined and non-predetermined. For a unique solution to exist, it must be that the number of non-predetermined variables is equal to the number of explosive eigenvalues (so that we have \"saddle-path\" stability).\n",
    "\n",
    "Here, $k_{t+1}$ is predetermined, and $c_{t+1}$ is non-predetermined, so we have a unique solution if there is one explosive eigenvalue and one non-explosive eigenvalue.\n",
    "\n",
    "Finally we can express the coefficients in the policy function above, as (see for example DeJong and Dave, section 4.3):\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\phi_{ck} & = - \\Lambda_{22}^{-1} \\Lambda_{21}\\\\\n",
    "\\phi_{cz} & = - \\Lambda_{22}^{-1} J_2^{-1} \\left (I - \\rho J_2^{-1} D_2 \\right )^{-1} \\\\\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where $D_2$ is the lower component of $D = \\Lambda \\mathbf{C}$. Here, $D_2 = \\Lambda_{21} C_1 + \\Lambda_{22} C_2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def ordered_jordan_decomposition(matrix):\n",
    "    # Get eigenvalues and left eigenvectors of `matrix`\n",
    "    # Note that the left eigenvectors of `matrix`\n",
    "    # are the transpose of the right eigenvectors of the\n",
    "    # transpose of matrix, and that `matrix` and `matrix'`\n",
    "    # have the same eigenvalues\n",
    "    eigenvalues, right_eigenvectors = np.linalg.eig(matrix.transpose())\n",
    "    left_eigenvectors = right_eigenvectors.transpose()\n",
    "    \n",
    "    # Sort in increasing order\n",
    "    idx = np.argsort(eigenvalues)\n",
    "    \n",
    "    # Return eigenvector matrix, diagonal eigenvalue matrix\n",
    "    # Note that the left eigenvectors are in the rows now,\n",
    "    # not the columns\n",
    "    return np.diag(eigenvalues[idx]), left_eigenvectors[idx, :]\n",
    "\n",
    "def solve_blanchard_kahn(B, C, rho, k_predetermined):\n",
    "    # Perform the Jordan decomposition\n",
    "    # this yields J, Lambda\n",
    "    eigenvalues, left_eigenvectors = ordered_jordan_decomposition(B)\n",
    "    left_eigenvectors = left_eigenvectors\n",
    "\n",
    "    # Dimensions\n",
    "    k_variables = len(B)\n",
    "    k_nonpredetermined = k_variables - k_predetermined\n",
    "\n",
    "    k_stable = len(np.where(eigenvalues.diagonal() < 1)[0])\n",
    "    k_unstable = k_variables - k_stable\n",
    "\n",
    "    # Make sure we're saddle-path stable\n",
    "    if not k_unstable == k_nonpredetermined:\n",
    "        raise RuntimeError('Blanchard-Kahn condition not met. Unique solution does not exist')\n",
    "\n",
    "    # Decouple the system (this is D, above)\n",
    "    decoupled_C = np.dot(left_eigenvectors, C)\n",
    "\n",
    "    # Create partition indices\n",
    "    p1 = np.s_[:k_predetermined]\n",
    "    p2 = np.s_[k_predetermined:]\n",
    "\n",
    "    p11 = np.s_[:k_predetermined, :k_predetermined]\n",
    "    p12 = np.s_[:k_predetermined, k_predetermined:]\n",
    "    p21 = np.s_[k_predetermined:, :k_predetermined]\n",
    "    p22 = np.s_[k_predetermined:, k_predetermined:]\n",
    "\n",
    "    # Solve the explosive component (controls) in terms of the\n",
    "    # non-explosive component (states) and shocks\n",
    "    tmp = np.linalg.inv(left_eigenvectors[p22])\n",
    "    \n",
    "    # This is \\phi_{ck}, above\n",
    "    policy_state = - np.dot(tmp, left_eigenvectors[p21])\n",
    "    # This is \\phi_{cz}, above\n",
    "    policy_shock = -(\n",
    "        np.dot(tmp, 1. / eigenvalues[p22]).dot(\n",
    "            np.linalg.inv(\n",
    "                np.eye(k_nonpredetermined) -\n",
    "                rho / eigenvalues[p22]\n",
    "            )\n",
    "        ).dot(decoupled_C[p2])\n",
    "    )\n",
    "\n",
    "    # Solve for the non-explosive transition\n",
    "    # This is T_{kk}, above\n",
    "    transition_state = B[p11] + np.dot(B[p12], policy_state)\n",
    "    # This is T_{kz}, above\n",
    "    transition_shock = np.dot(B[p12], policy_shock) + C[p1]\n",
    "    \n",
    "    return policy_state, policy_shock, transition_state, transition_shock\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ReducedRBC2(ReducedRBC1):\n",
    "    def solve(self, params=None):\n",
    "        # Update the model parameters, if given\n",
    "        if params is not None:\n",
    "            self.update(params)\n",
    "        \n",
    "        # Solve the model\n",
    "        phi_ck, phi_cz, T_kk, T_kz = solve_blanchard_kahn(\n",
    "            self.B(), self.C(),\n",
    "            self.technology_shock_persistence, 1\n",
    "        )\n",
    "        \n",
    "        # Create the design matrix\n",
    "        inv_capital_share = 1. / self.capital_share\n",
    "        tmp1 = (1 - self.capital_share) * inv_capital_share\n",
    "        phi_yk = 1 - tmp1 * phi_ck\n",
    "        phi_yz = inv_capital_share - tmp1 * phi_cz\n",
    "        phi_nk = 1 - inv_capital_share * phi_ck\n",
    "        phi_nz = inv_capital_share * (1 - phi_cz)\n",
    "        design = np.r_[\n",
    "            phi_yk, phi_yz, phi_nk, phi_nz, phi_ck, phi_cz\n",
    "        ].reshape((3,2))\n",
    "        \n",
    "        # Create the transition matrix\n",
    "        transition = np.r_[\n",
    "            T_kk[0,0], T_kz[0,0], 0, self.technology_shock_persistence\n",
    "        ].reshape((2,2))\n",
    "        \n",
    "        return design, transition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modulus of eigenvalues of B: [ 0.884  1.191]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\phi_{ck}$</th>\n",
       "      <th>$\\phi_{cz}$</th>\n",
       "      <th>$T_{kk}$</th>\n",
       "      <th>$T_{kz}$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>solution</th>\n",
       "      <td>0.534</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          $\\phi_{ck}$  $\\phi_{cz}$  $T_{kk}$  $T_{kz}$\n",
       "solution        0.534        0.487     0.884     0.319"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reduced_mod2 = ReducedRBC2(parameters['value'])\n",
    "\n",
    "# Check the Blanchard-Kahn conditions\n",
    "eigenvalues, left_eigenvectors = ordered_jordan_decomposition(reduced_mod2.B())\n",
    "print 'Modulus of eigenvalues of B:', np.abs(eigenvalues.diagonal())\n",
    "\n",
    "# Test the eigenvalues and eigenvectors\n",
    "# (results from \"Emsm\", downloaded from\n",
    "# https://sites.google.com/site/frugemurcia/home/replication-files\n",
    "# on 06/19/2015)\n",
    "assert_allclose(eigenvalues.diagonal(), [0.88408644, 1.1906433])\n",
    "assert_allclose(left_eigenvectors[0,:], [0, 1])\n",
    "# Note the second eigenvector returned by numpy\n",
    "# is scaled differently than the one returned\n",
    "# by Gauss, so we need to rescale it to compare.\n",
    "assert_allclose(\n",
    "    left_eigenvectors[1,:] / left_eigenvectors[1,1],\n",
    "    [-0.53406267, 1]\n",
    ")\n",
    "\n",
    "# Solve the model\n",
    "design, transition = reduced_mod2.solve()\n",
    "\n",
    "# Test the solved model coefficients for accuracy\n",
    "assert_allclose(design[2,:], [0.53406267, 0.48719795])\n",
    "\n",
    "# Display the results\n",
    "display(pd.DataFrame(\n",
    "    np.r_[design[2,:], transition[0,:]],\n",
    "    columns=['solution'],\n",
    "    index=[r'$\\phi_{ck}$', r'$\\phi_{cz}$', r'$T_{kk}$', r'$T_{kz}$']\n",
    ").T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## State-space form\n",
    "\n",
    "Assuming that $\\xi_t = (\\tilde k_t, \\tilde z_t)'$ is unobservable and $s_t = (\\tilde y_t, \\tilde n_t, \\tilde c_t)'$ is observable, the above equations give use the following relationship:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\begin{bmatrix}\n",
    "\\tilde y_t \\\\\n",
    "\\tilde n_t \\\\\n",
    "\\tilde c_t \\\\\n",
    "\\end{bmatrix} & = \\begin{bmatrix}\n",
    "\\phi_{yk} & \\phi_{yz} \\\\\n",
    "\\phi_{nk} & \\phi_{nz} \\\\\n",
    "\\phi_{ck} & \\phi_{cz} \\\\\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\tilde k_{t} \\\\\n",
    "\\tilde z_{t}\n",
    "\\end{bmatrix} \\\\\n",
    "& = \\begin{bmatrix}\n",
    "1 - \\frac{1-\\alpha}{\\alpha} \\phi_{ck} & \\frac{1}{\\alpha} - \\frac{1-\\alpha}{\\alpha} \\phi_{cz} \\\\\n",
    "1 - \\frac{1}{\\alpha} \\phi_{ck} & \\frac{1}{\\alpha} \\left [ 1 - \\phi_{cz} \\right ] \\\\\n",
    "\\phi_{ck} & \\phi_{cz} \\\\\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\tilde k_{t} \\\\\n",
    "\\tilde z_{t}\n",
    "\\end{bmatrix}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "The above is the observation equation. In order to overcome the stochastic singularity problem, we augment with measurement errors $u_t = (u_{yt}, u_{nt}, u_{ct})'$:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "s_t & = \\mathbf{\\Phi} \\xi_t + u_t\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "And the transition equation is:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\begin{bmatrix}\n",
    "\\tilde k_{t+1} \\\\\n",
    "\\tilde z_{t+1} \\\\\n",
    "\\end{bmatrix} & = \\begin{bmatrix}\n",
    "T_{kk} & T_{kz}\\\\\n",
    "0 & \\rho \\\\\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\tilde k_{t} \\\\\n",
    "\\tilde z_{t}\n",
    "\\end{bmatrix} + \\begin{bmatrix}\n",
    "0 \\\\\n",
    "1\n",
    "\\end{bmatrix}\n",
    "\\varepsilon_t\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Python package `statsmodels` has support for maximum likelihood analysis of state space models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibration\n",
    "\n",
    "There are six parameters\n",
    "\n",
    "$$\\{ \\beta, \\psi, \\delta, \\alpha, \\rho, \\sigma^2 \\}$$\n",
    "\n",
    "and of those three will be calibrated: $\\{ \\psi = 3, \\delta = 0.025, \\alpha = 0.36 \\}$ (note that $\\psi$ only influences the steady-state labor supply and so does not enter into the state-space form parameters). The calibration is done because the model has difficulty estimating all of the parameters, see Ruge-Murcia (2007) for more details.\n",
    "\n",
    "The remaining three parameters, $\\{ \\beta, \\rho, \\sigma^2 \\}$, will be estimated by maximum likelhood."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "We will evaluate the model using two datasets. The first will be simulated, allowing us to explore the capacity of the model to recover the true parameters. The second will estimate the model on observed economic data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulated dataset\n",
    "\n",
    "As in Ruge-Murcia (2007), we can simulate the linear model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(12345)\n",
    "\n",
    "# Parameters\n",
    "T = 200   # number of periods to simulate\n",
    "T0 = 100  # number of initial periods to \"burn\"\n",
    "\n",
    "# We can use the exact random draws from \"Emsm\", downloaded from\n",
    "# https://sites.google.com/site/frugemurcia/home/replication-files\n",
    "# on 06/19/2015)\n",
    "rm2007_eps = [0.0089954547, 0.069601997, -0.0081704445, -0.036704078, -0.026966673, -0.013741121, 0.0089339760, -0.0056557030, -0.0073353523, 0.027214134, 0.0036223219, -0.033331014, 0.032539993, 0.044695276, 0.012599442, -0.020012497, -0.065070833, 0.024777248, -0.058297234, -0.072139533, 0.080062379, 0.023164655, -0.028318809, 0.023734384, -0.023575740, 0.058697373, -0.00080918191, 0.029482310, 0.059178715, -0.010752551, 0.049127695, 0.063137227, -0.015733529, 0.018006224, 0.051256459, -0.014467873, 0.042611930, -0.078176552, -0.0040812905, -0.0086694118, 0.016261678, 0.0055330257, 0.026286130, -0.0066732973, 0.019133914, 0.018442169, 0.0046151171, 0.0015229921, 0.047776839, -0.058401266, 0.014895019, -0.0070732464, -0.036637349, 0.018778403, 0.0030934044, -0.033385312, -0.0044036385, -0.0029289904, -0.029415234, -0.010308393, -0.023496361, -0.023784028, 0.045396730, -0.021532569, -0.086991302, 0.046579589, 0.015086674, 0.0054060766, 0.0094114004, 0.014372645, -0.060998265, -0.0047493261, -0.030991307, -0.022061370, -0.020225482, -0.013470628, -0.013967446, -0.021552474, -0.054801903, -0.0052111107, 0.0080784668, 0.042868645, -0.0015220824, -0.061354829, 0.053529145, -0.020002403, -0.00053686088, 0.085988265, 0.037919020, 0.023531373, 0.0046336046, 0.012880821, 0.0037651140, -0.059647623, -0.027420909, -0.063257854, -0.010324261, -0.025627797, -0.017646345, -0.00091871809, 0.0066086013, 0.0018793222, 0.019543168, -0.031823750, -0.0092249652, 0.013246704, 0.014181125, 0.047271352, 0.047259268, 0.010107337, -0.083925083, -0.036031657, -0.0022387325, -0.035090684, -0.022218572, -0.017554625, 0.033953597, 0.010744674, -0.010891498, -0.0035293110, -0.033522281, -0.072168448, -0.0042416089, -0.025190520, 0.11066349, 0.029308577, -0.018047271, 0.055748729, -0.0016904632, -0.035578602, -0.10830804, -0.013671301, -0.010389470, -0.012295055, 0.055696357, 0.020597878, 0.026447061, -0.054887926, -0.045563156, 0.060229793, 0.028380999, -0.0034341303, 0.038103203, 0.012224323, 0.016752740, -0.0065436404, -0.0010711498, -0.025486203, -0.055621838, 0.0096008728, -0.088779172, 0.092452909, 0.057714587, -0.0057425132, 0.023627700, -0.029821882, -0.012037717, -0.074682148, -0.062682990, -0.038800349, -0.094946077, 0.074545642, -0.00050272713, -0.0075839744, -0.037362343, 0.012332294, 0.10490393, 0.049997520, 0.033916235, -0.061734224, -0.015363425, 0.057711167, -0.051687840, 0.031219589, 0.041031894, 0.0051038726, -0.013144180, 0.054156433, -0.0090438895, 0.023331707, -0.0079434321, -0.0029084658, -0.0064262300, 0.044577448, 0.014816901, 0.043276307, -0.011412684, -0.0026201902, -0.021138420, -0.0020795206, -0.042017897, -0.028148295, 0.063945871, -0.049724502, -0.048571001, -0.061207381, 0.050007129, 0.0062884061, 0.057948665, -0.012780170, -0.020464058, 0.023577863, 0.030007840, -0.013682281, 0.044281158, 0.033864209, -0.016235593, 0.0052712906, 0.035426922, -0.084935662, -0.061241657, 0.038759520, 0.019838792, -0.038971482, -0.043112193, -0.10098203, 0.011744644, 0.014708720, 0.035224935, 0.0098378679, 0.031205446, 0.026015597, -0.048897576, -0.042539822, -0.036330332, -0.033689415, 0.029665808, 0.0086127051, 0.038663112, -0.064534479, -0.036174560, -0.034225451, -0.0084848888, -0.011724560, -0.037544322, -0.013054490, -0.062983798, 0.011448707, 0.0022791918, -0.054508196, 0.046134801, -0.063884585, 0.048918326, 0.018358644, -0.011278321, 0.021175611, -0.0069196463, -0.084987826, 0.016286265, -0.031783692, -0.041129528, -0.11686860, 0.0040626993, 0.057649830, 0.019174675, -0.010319778, 0.080549326, -0.058124228, -0.027757539, -0.0028474062, 0.012399938, -0.088780901, 0.077048657, 0.070548177, -0.023784957, 0.035935388, 0.064960358, 0.019987594, 0.062245578, 0.0014217956, 0.057173164, 0.043800495, -0.023484057, 0.021398628, -0.012723988, 0.012587101, -0.049855702, 0.070557277, -0.017640273, -0.031555592, -0.030900124, -0.028508626, -0.029129143, 0.0024196883, -0.026937200, -0.011642554, -0.045071194, -0.013049519, -0.021908382, 0.017900266, -0.019798107, -0.040774046, -0.027013698, 0.065691125, 0.0081570086, -0.012601818, 0.017918061, 0.017225503, 0.0021227212, 0.032141622, 0]\n",
    "# Or we can draw our own\n",
    "gen_eps = np.random.normal(0, reduced_mod1.technology_shock_std, size=(T+T0+1))\n",
    "\n",
    "eps = rm2007_eps\n",
    "\n",
    "# Create and solve the model\n",
    "reduced_mod2 = ReducedRBC2(parameters['value'])\n",
    "design, transition = reduced_mod2.solve()\n",
    "selection = np.array([0, 1])\n",
    "\n",
    "# Generate variables\n",
    "raw_observed = np.zeros((T+T0+1,3))\n",
    "raw_state = np.zeros((T+T0+2,2))\n",
    "\n",
    "for t in range(T+T0+1):\n",
    "    raw_observed[t] = np.dot(design, raw_state[t])\n",
    "    raw_state[t+1] = np.dot(transition, raw_state[t]) + selection * eps[t]\n",
    "\n",
    "# Test that our simulated series are the same as in \"Emsm\"\n",
    "# Note: Gauss uses ddof=1 for std dev calculation\n",
    "assert_allclose(np.mean(raw_state[1:-1,:], axis=0), [-0.0348286036, -0.0133121934])\n",
    "assert_allclose(np.std(raw_state[1:-1,:], axis=0, ddof=1), [0.122766006, 0.0742206044])\n",
    "assert_allclose(np.mean(raw_observed[1:,:], axis=0), [-0.027208998, -0.0021226675, -0.025086330])\n",
    "assert_allclose(np.std(raw_observed[1:,:], axis=0, ddof=1), [0.14527028, 0.089694148, 0.090115364])\n",
    "\n",
    "# Drop the first 100 observations\n",
    "sim_observed = raw_observed[T0+1:,:]\n",
    "sim_state = raw_state[T0+1:-1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Qw52qqgRDoW7LKxo6CIbUHrvTAqQnSYZTDI4+BZzr1q3D5/OxcuVK7rnnHh5//PHIumAw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LTOk6MZnyoMRqMoA46ecB+iII7IIa+z1WDSmjjobs67L1WdpvccG5iUc9M6XdhXnE+ir4/Alk2T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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fadffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13,4))\n",
    "\n",
    "ax.plot(sim_observed[:,0], label='Output')\n",
    "ax.plot(sim_observed[:,1], label='Labor')\n",
    "ax.plot(sim_observed[:,2], label='Consumption')\n",
    "\n",
    "ax.set_title('Simulated observed series')\n",
    "ax.xaxis.grid()\n",
    "ax.legend(loc='lower left')\n",
    "\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sjl9IbvQc9BrdiM9T7u1QO8wdToDc6DmoUFHYepIr0leP+Jr+MDc9kpBALQdK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NAAAg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\nIiIiIiJSIdRwioiIiIiISIX4f2elPa8SdsxUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fbc6910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13,4))\n",
    "\n",
    "ax.plot(sim_state[:,0], label='Capital')\n",
    "ax.plot(sim_state[:,1], label='Technology shock')\n",
    "\n",
    "ax.set_title('Simulated unobserved states')\n",
    "ax.xaxis.grid()\n",
    "ax.legend(loc='lower left')\n",
    "\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observed economic data\n",
    "\n",
    "We use data on hours worked, consumption, and investment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get some data\n",
    "start='1984-01'\n",
    "end = '2015-01'\n",
    "labor = DataReader('HOANBS', 'fred', start=start, end=end)        # hours\n",
    "consumption = DataReader('PCECC96', 'fred', start=start, end=end) # billions of dollars\n",
    "investment = DataReader('GPDI', 'fred', start=start, end=end)     # billions of dollars\n",
    "population = DataReader('CNP16OV', 'fred', start=start, end=end)  # thousands of persons\n",
    "recessions = DataReader('USRECQ', 'fred', start=start, end=end)\n",
    "\n",
    "# Collect the raw values\n",
    "raw = pd.concat((labor, consumption, investment, population.resample('QS').mean()), axis=1)\n",
    "raw.columns = ['labor', 'consumption', 'investment', 'population']\n",
    "raw['output'] = raw['consumption'] + raw['investment']\n",
    "\n",
    "# Make the data consistent with the model\n",
    "y = np.log(raw.output * 10**(9-3) / raw.population)\n",
    "n = np.log(raw.labor * (1e3 * 40) / raw.population)\n",
    "c = np.log(raw.consumption * 10**(9-3) / raw.population)\n",
    "\n",
    "# Make the data stationary\n",
    "y = y.diff()[1:]\n",
    "n = n.diff()[1:]\n",
    "c = c.diff()[1:]\n",
    "\n",
    "# Construct the final dataset\n",
    "econ_observed = pd.concat((y, n, c), axis=1)\n",
    "econ_observed.columns = ['output','labor','consumption']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d2CwmNjWU4BuPFPj5k/29YDRia23FfeMelFCIwMnT/PClLkxGA/fs0QuZgy//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rhiVQ2fumsVXVkjI8p396fHf4UkSbxv+btxtc641AxUaDLtcpDHVLwR+SKab7RGJapwG1xFNQRK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\nRGQUU0AgIiIiIjKKKSAQERERERnF/n/lJRX10mQpogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb650d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13,4))\n",
    "\n",
    "dates = econ_observed.index._mpl_repr()\n",
    "\n",
    "ax.plot(dates, econ_observed.output, label='Output')\n",
    "ax.plot(dates, econ_observed.labor, label='Labor')\n",
    "ax.plot(dates, econ_observed.consumption, label='Consumption')\n",
    "\n",
    "rec = recessions.resample('QS').last().ix[econ_observed.index[0]:,0].values\n",
    "ylim = ax.get_ylim()\n",
    "ax.fill_between(dates, ylim[0]+1e-5, ylim[1]-1e-5, rec, facecolor='k', alpha=0.1)\n",
    "\n",
    "ax.xaxis.grid()\n",
    "ax.legend(loc='lower left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class EstimateRBC1(sm.tsa.statespace.MLEModel):\n",
    "    def __init__(self, output=None, labor=None, consumption=None,\n",
    "                 measurement_errors=True,\n",
    "                 disutility_labor=3, depreciation_rate=0.025,\n",
    "                 capital_share=0.36, **kwargs):\n",
    "\n",
    "        # Determine provided observed variables\n",
    "        self.output = output is not None\n",
    "        self.labor = labor is not None\n",
    "        self.consumption = consumption is not None\n",
    "        self.observed_mask = (\n",
    "            np.array([self.output, self.labor, self.consumption], dtype=bool)\n",
    "        )\n",
    "        \n",
    "        observed_variables = np.r_[['output', 'labor', 'consumption']]\n",
    "        self.observed_variables = observed_variables[self.observed_mask]\n",
    "        \n",
    "        self.measurement_errors = measurement_errors\n",
    "        \n",
    "        # Construct the full endogenous array\n",
    "        endog = []\n",
    "        if self.output:\n",
    "            endog.append(np.array(output))\n",
    "        if self.labor:\n",
    "            endog.append(np.array(labor))\n",
    "        if self.consumption:\n",
    "            endog.append(np.array(consumption))\n",
    "        endog = np.c_[endog].transpose()\n",
    "        \n",
    "        # Initialize the statespace model\n",
    "        super(EstimateRBC1, self).__init__(endog, k_states=2, k_posdef=1, **kwargs)\n",
    "        self.initialize_stationary()\n",
    "        self.data.ynames = self.observed_variables\n",
    "        \n",
    "        # Check for stochastic singularity\n",
    "        if self.k_endog > 1 and not measurement_errors:\n",
    "            raise ValueError('Stochastic singularity encountered')\n",
    "        \n",
    "        # Save the calibrated parameters\n",
    "        self.disutility_labor = disutility_labor\n",
    "        self.depreciation_rate = depreciation_rate\n",
    "        self.capital_share = capital_share\n",
    "        \n",
    "        # Create the structural model\n",
    "        self.structural = ReducedRBC2()\n",
    "        \n",
    "        # Setup fixed elements of the statespace matrices\n",
    "        self['selection', 1, 0] = 1\n",
    "        \n",
    "        idx = np.diag_indices(self.k_endog)\n",
    "        self._idx_obs_cov = ('obs_cov', idx[0], idx[1])\n",
    "        \n",
    "    @property\n",
    "    def start_params(self):\n",
    "        start_params = [0.99, 0.5, 0.01]\n",
    "        if self.measurement_errors:\n",
    "            start_meas_error = np.r_[[0.1]*3]\n",
    "            start_params += start_meas_error[self.observed_mask].tolist()\n",
    "        \n",
    "        return start_params\n",
    "\n",
    "    @property\n",
    "    def param_names(self):\n",
    "        param_names = ['beta', 'rho', 'sigma.vareps']\n",
    "        if self.measurement_errors:\n",
    "            meas_error_names = np.r_[['sigma2.y', 'sigma2.n', 'sigma2.c']]\n",
    "            param_names += meas_error_names[self.observed_mask].tolist()\n",
    "        \n",
    "        return param_names\n",
    "    \n",
    "    def transform_params(self, unconstrained):\n",
    "        constrained = np.zeros(unconstrained.shape, unconstrained.dtype)\n",
    "        \n",
    "        # Discount rate is between 0 and 1\n",
    "        constrained[0] = max(1 / (1 + np.exp(unconstrained[0])) - 1e-4, 1e-4)\n",
    "        # Technology shock persistence is between -1 and 1\n",
    "        constrained[1] = unconstrained[1] / (1 + np.abs(unconstrained[1]))\n",
    "        # Technology shock std. dev. is positive\n",
    "        constrained[2] = np.abs(unconstrained[2])\n",
    "        # Measurement error variances must be positive\n",
    "        if self.measurement_errors:\n",
    "            constrained[3:3+self.k_endog] = unconstrained[3:3+self.k_endog]**2\n",
    "        \n",
    "        return constrained\n",
    "    \n",
    "    def untransform_params(self, constrained):\n",
    "        unconstrained = np.zeros(constrained.shape, constrained.dtype)\n",
    "        \n",
    "        # Discount rate is between 0 and 1\n",
    "        unconstrained[0] = np.log((1 - constrained[0] + 1e-4) / (constrained[0] + 1e-4))\n",
    "        # Technology shock persistence is between -1 and 1\n",
    "        unconstrained[1] = constrained[1] / (1 + constrained[1])\n",
    "        # Technology shock std. dev. is positive\n",
    "        unconstrained[2] = constrained[2]\n",
    "        # Measurement error variances must be positive\n",
    "        if self.measurement_errors:\n",
    "            unconstrained[3:3+self.k_endog] = constrained[3:3+self.k_endog]**0.5\n",
    "        \n",
    "        return unconstrained\n",
    "    \n",
    "    def update(self, params, **kwargs):\n",
    "        params = super(EstimateRBC1, self).update(params, **kwargs)\n",
    "        \n",
    "        # Get the parameters of the structural model\n",
    "        # Note: we are calibrating three parameters\n",
    "        structural_params = np.r_[\n",
    "            params[0],\n",
    "            self.disutility_labor,\n",
    "            self.depreciation_rate,\n",
    "            self.capital_share,\n",
    "            params[1:3]\n",
    "        ]\n",
    "        \n",
    "        # Solve the model\n",
    "        design, transition = self.structural.solve(structural_params)\n",
    "        \n",
    "        # Update the statespace representation\n",
    "        self['design'] = design[self.observed_mask, :]\n",
    "        if self.measurement_errors:\n",
    "            self[self._idx_obs_cov] = params[3:3+self.k_endog]\n",
    "        self['transition'] = transition\n",
    "        self['state_cov', 0, 0] = self.structural.technology_shock_std**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimation on simulated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  Statespace Model Results                                  \n",
      "============================================================================================\n",
      "Dep. Variable:     ['output' 'labor' 'consumption']   No. Observations:                  200\n",
      "Model:                                 EstimateRBC1   Log Likelihood                3106.974\n",
      "Date:                              Fri, 22 Sep 2017   AIC                          -6201.949\n",
      "Time:                                      21:40:32   BIC                          -6182.159\n",
      "Sample:                                           0   HQIC                         -6193.940\n",
      "                                              - 200                                         \n",
      "Covariance Type:                                opg                                         \n",
      "================================================================================\n",
      "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "beta             0.9501   8.52e-06   1.12e+05      0.000       0.950       0.950\n",
      "rho              0.8502   3.85e-05   2.21e+04      0.000       0.850       0.850\n",
      "sigma.vareps     0.0628   3.46e-08   1.82e+06      0.000       0.063       0.063\n",
      "sigma2.y      1.109e-10   8.23e-09      0.013      0.989    -1.6e-08    1.62e-08\n",
      "sigma2.n      1.114e-11    9.2e-09      0.001      0.999    -1.8e-08     1.8e-08\n",
      "sigma2.c      1.215e-05   1.69e-05      0.717      0.473   -2.11e-05    4.54e-05\n",
      "======================================================================================\n",
      "Ljung-Box (Q):          40.68, 385.51, 385.73   Jarque-Bera (JB):     0.09, 0.77, 0.77\n",
      "Prob(Q):                     0.44, 0.00, 0.00   Prob(JB):             0.95, 0.68, 0.68\n",
      "Heteroskedasticity (H):      0.86, 1.66, 1.66   Skew:               -0.05, -0.05, 0.04\n",
      "Prob(H) (two-sided):         0.54, 0.04, 0.04   Kurtosis:             2.95, 2.71, 2.71\n",
      "======================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 2.23e+21. Standard errors may be unstable.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fulton/.virtualenvs/default/lib/python2.7/site-packages/ipykernel/__main__.py:75: RuntimeWarning: overflow encountered in exp\n",
      "/Users/fulton/projects/statsmodels/statsmodels/base/model.py:511: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "# Setup the statespace model\n",
    "sim_mod = EstimateRBC1(\n",
    "    output=sim_observed[:,0],\n",
    "    labor=sim_observed[:,1],\n",
    "    consumption=sim_observed[:,2],\n",
    "    measurement_errors=True\n",
    ")\n",
    "\n",
    "# sim_res = sim_mod.fit(maxiter=1000, information_matrix_type='oim')\n",
    "sim_res = sim_mod.fit(maxiter=1000)\n",
    "\n",
    "print sim_res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Z9evX88tf/pIXX3zRvs1kMvHKK6/wj3/8g48//pjm5mZ27do1agMWQgghhBBCOJcOi5WP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\nY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OJr1NFgaiLcK4SgQDFBmy6+Om/arR3jPk+dspMzpkIUspqaMgOnqjrYvGzytigHT9Wz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Lh9/GaDeR4BtPa6eeUFUwMd5RJz2W2W7h5cNvc6gph/2NhwhXH/Pu1XTUsbzkc0S7ghtG3UxU\n0LE6Aw+pB2FeIUR7RxLvG0Oi/yjGhWRS21HH0dZiOp020gKTj+0vlzIuKZiCylYKq/VMTQsdUEH1\ndLA7nHyWvRNpUAM2l42C1kLumTWfhiYb+o5OHrgqg9hQd7qLzqIn3N+HTQfrsdoczMgcGWNkpNlc\n+wMtFh1LRl/Oym1VWG1OfnVx8gm9xHVaK9W2AkzODi6MvaBPRPTTgjWYBT0XRyxEEES0tjpcHQFM\njI/rM1Z+RSvbjzQwekwrlXZ31OqujFtJD0yhrK2KAl0RR5rzSAtIwUuuwt/bg7gwb2ZkhuOrgLr/\nvoiH1UHLLZfwSaKBusmjWHTb0/jPnY//hRcj8zmxx/h4JDIZSWMTOegMYqU1moN+aVx5cQYh/kN7\nXpe3VfFD3W4SfOOGVKMlCAIKuZRDJc1sz2mgzdhJfLgPSoX7WM+kZEy5OZjzclFEReMRPvCiqn3f\nHpwmE/KgU/Oyn0m+21/D+r015FfqmJkZgVzm/s05nC4+XF+EWiVn3DgJrx55B4vDyuJRC1mg8ab9\nnffILLUw6mgLupI8PCx2HAYDxkPZtG3bgm7Nato2b0SQyYh86Leox45DEAQEmQxL7mFMNhcpc87s\nc3ckKNWX81rOexjtZpYkXs7Y4AxymvPxVqj7zTr4heHF4rDy2pH3qDdqmBs1k8UJC/FSqJgaPhGF\nRE6+rgituYk4nxge7jI2jydUFYzNaaO0rYIEvzhCVcOXqj9cfF+9hSZLC9cmXYHyR7WpvgpvNtf+\ngMVhZXbUtGE974aqTTSam7gu+UrUci++3FaO3eFCFMHa6WBS6rG1al2TkVU/VJA5KpD5E068jrGU\nltC6ZjVeWWPwv+iS056rIAhMC59EmFcobZ0GStrK2d94iILWYiaEjEUmGVpipb+3B1PSQpHLhlbX\nG+ijZOOBWtqMtgGvW5WWjqkgH3NeLnL/AJSxcUO9rJ88Bbpi9moO4hAd+Hn4DPiu9vI69cjwLxFO\n3A/KnfX78FaosTe7F8oDpdN2E6l2L3Iq9QPXbP1U4fz4cAAAIABJREFUaTS5PeIhx70QnC4X768r\nZHf+sabxc6JmcG/WbZTWuqMUyQM0H56Zkoiz3R+to/a0+zedDMcbmzckX8WtqdcBkNt88o2CHS4H\n7+Z/TKO5iRR/t0T6Gznv82XpN5jtZj7IX44ouJBrxjFx9IlfAhJBwu3pNxKqCmFL7Q72ag722af7\nobr50MhIsVc1diD4uxVQp3ep4L1dsJS7r0rgPw/OICM+kBaLjndyP+LpPc+xs2UriVG+FNe0oe/o\nHJE5jTQNRg3+Hn5YzAL1zSZSYvzxkJ/4hRcd7I3L6IfZaaLqR3WcRpuJFqpwWdRMi09lSnQmAJXG\n/j3hu/M1CEoTdbJs1HIvbki+GkEQiPWJ5o+TfsvcqJlozc18cFz0e8zoIOLDfSj/7APUbVaqx0Qw\ne/YNTA+fTL1RwybtXuQBgUOWx/8xki7F5PkTokiO9iM1tm+2Qn+0WHS8mfs+G2u2samrZGEozMoK\n55FrxxAWoGL7kQaefHsPa/dUYXc4kcjlhN19H4Jcjvaj97Fp+49gte/eReO7b1P/8kvnfN2Oze5k\n4wH376a1vZNPNh6rPSyq1mPudDA+OZAvS1fjcDm5J/NXTG30oOl/y5B6e6OcPAmnVILH0QqaPv0f\nDa+9TMuqL+nYtxd7cxPKUQlEPfoEXhnu316FoQrb2CQsMk9GNeThtJzfdZzFrWW8lrMUm9POHWk3\nMi96FplBacgkMg435Z3RuRxqyuVPO/+PJvPPp3+sw+Xg7dwPqemoY2r4RK5OvKznWSMRJFwUN5cn\nJj7MZfEX8dDYO/GU9d8yaZRfHOB2Up1t1lZu5IWDr7G24ntqO+pxupyUtVUR7BmIn0dfIR65VM5o\nv1E0mBqHVXlXFEXKDVX4efgSqgrGaLGj7+gkLS6AmFA12SXNtBzXT3fbEfc7e87YE0c3AVq7o5uL\nLh+2OUslUiaGjuXxiQ/xxMSHSAtMprq9lh31I6fV4aGQkjEqEI3OTEOLqd99JHI5Efc9gETlRdOn\nH2Otrhqx+ZwvmOxmPjr6GW/kLOW76i28kfM+v9/xN1478h5ba3didw6fivkvBiewq2EfVqeVOVEz\nySlrRSoR+m3yfjyJgW6FyQazZtD9foo0md0pC8d7IA8UNrEzV8Oy74ppbT/WK8/pclFS10ZYgGrA\nerUQP098Ot0pKjvq9o/gzI9Rb9QcZ2xezazIaQSrAonwCqNIX4rVMXSDSRRFlhetokRfRlZQOg+O\nvZPHJz5MqCqErbU7eWr38zRZmnE0xjIzPqtH5fVEeMo8uS/rNjxlniwvWkmloXe65oTkYPzUCnbl\nabB0Dn+PqYLaRiS+LQTIQrg59Vouip1Lk6WF13PewyU1s6Z8A/+378UeMYMddXsYl+qLCOw7D8WD\njHYTBlsHEeowcrvSaQdrh3I8UcFqHE3RIMJ7+R/3SsveXX8QBBd+nQkoFTLSgkYjuGR0yOr7tJEx\nWe0cLm3GK7EAh+jg+uSreolTKKRyrkm6gslh46nuqGVD9ZaebeaiQsQde2n1kRJ7/a8AuDpxEX4e\nvqyv2tRLkKPF0sr6yk38ULd7yN+PRBC4+cIk/nDz+CH9hu1OO+/lfYzFYUUhkfN99Rb01rYhnUsQ\nBLISAvn7rydz84VJyKQSVm6v4LPNbrElj4gIQm6+FZfJRP3LL+Hs6J16a62pRvvxhyCXI9psNL7/\nHuIZENk6VXbmaWg327l4cjTx4d7sKWjkQJHbsXewK53WK7yJBlMjU8InEF9rpfH995B4ehL5u8eJ\nuedBah++mg8vD0B32XSCrr6GiId+S9xz/2b0q28S8+Rf8BztTvk70pzPS9lvsqx0FTVx4/Bw2WnZ\nunXAuZ3r2Jw2Pin6Apfo4v6sO5gY5q5J9ZQpSQtIpsHUiOYMpdW6RBfflK/HYOsgW3t2SkTOBnkt\nhZS2VTAmKJ2bU67pJSTYTbR3BAvjFwxobALE+8QgIFBhqBrB2Z6YUn056yo3UtVew7qqTTx/4GX+\nvOufWJ3WftNpu5kbPQuADwo+GfIaotGkZUPVZhyu/t/hbZ0GOmxGYn3ca87udNroEDUXToxGFI85\nnTtt7h7ffmoFWUN4d1krKzAX5OOZkopnwughzfdkifOJ4Y60m/CUKdlUs33ERJWAHlXuwdYf8qBg\nwu66G9HhQPPm6zhN/RunPwdymwv4x74X2d94iFjvaO7OuJULY+YQogqmsLWEL0u/YX/joWE738/e\n4HS6nGyt3YlCqmCs/3hqtEaSov3w9Bg87J8aGgdAm/OnU/8yVLTdBqeX++YWRZF1e93GkM3u4ott\n5T37Vjca6bQ5SYkZvBfUpLCxiE4puxsO4BJHdmFYb9TwyuF3MNpN3JS8hFmRU3u2ZQWl4XA5KGod\nurrhhqot7G08SIx3FLen34hEkBDtHcEfJv2GGRFTsDqtKBx+2GuTmHmSqaYhqmDuTL8Zp+ji7byP\nenl+ZVIJc8ZFYul09ojMDCc5LXkIEpEpYW4l4StGXcIFUdNpMDXy1O7n2FC9BbXcizvSbuTK0Zdi\nc9mxepcjlQjsPXpuR5T6Q3OcYNDRKj0AWSdwPHUTEaSCjmB8OjJp6zTwbt7HOFwORFFkR90+RJdA\nht8YwK3k7EcUgtJMbl3v3p07cjSIQRU4PVuZEDKG8SFZfc7VWV/HwjpvMrUyfij8nqr2GpwWC/VL\n38YlQP78JBKD3SnYnjJPbki+CqfodIt1aLL576G3+Oue5/m28ntWlHzNPk32KX9ng/F5yWpqjQ1M\nD5/MtUlXYnPZ+bp83UmNIZNKmD8hiufvnYqvWsGBoqaefrC+M2fjv3AR9iYt9a+/gsvuXsg4jUYa\n3ngV0W5nXdRc6kISsZaXof9+w7Bf43DgcLpYv7cGuY+Bw/LlpE7SoZBJWLahCJ3ByuHSZrzVUg60\n7UAukXGRJZrGd95EkMuJ/O2jKGNiAbgobi42f29WBdSjvHA+6rHjUASHIBznHChrq+SDgk8REant\nqMeUMRGbIMOweeN52xh9fdVmdFY986NnkxroVstuN9t48u09SNvdUZ5DTblnZC5HmvNptrhbKeXr\nis7IOc8FCrqu9eK4ef0am0NFKVMS5R1BdUddH4XtM4XNaefTopUICDw09i7uzLiFSaHjcIhu52B6\nUOqAx6YHJjMvehZaczNflKw+4bnyWwp54eBrrKn4bsDeldVdGTOxXWU+dccZnJNTQ/H1UvBDjtvp\nvL9Qi6XTyaysiBM6BUWXi5bVXwMQOIzRzf5QyT2ZEzUTo900olHO8YnBKBVSdudrcA1So6nOGkvA\nosuxtzTT+P6757QzciRwuBwsO7qCt/M+wmw3szhhIY9NeICxIZlcOfpS/jT5d/xzxp+5J/M2JoUN\nn6jcz97gzG7Koa3TwPTwSVTVuyNzaXEnThfzV/oi2JV0yvQjPcVzjm6DszulNqdcR12ziSlpocSH\ne7PvqJaSWncko7jW/f0kncjgTA7HqQvH5OygRF8+6L6nQ11HQ09k86aUJcyIdNcuuVwiL352mPpy\ndzQpt2VoabUHtUf4tvI7ApT+3Jd1B2U1Rn7/5m5W/VCB0y7hppQl3Jd6L+0540mI8Cc8cOD2Gv0h\niiLJPnFcH30JtLXz8eZX2Lj5Q9r278Wwcwcz41TIpAJbDtUNaxG8KIo0CaUgwqxYd98qQRC4JvEK\nZkRMRiaRcknsPJ6e+gQTw8YxM2IKnjIle7R7SYv3oUZrHDCt5Vyl3nRMMKipzYKHXEqg79BUUOUy\nKaEBnrRXRjM+JIsKQxWfl3xNZXsNrfYWnPpQ0qKO1dkk+bijTfvrj6X7OV0uNuYdRRZVilruxXVJ\nV/Zsszc307ruW6r++heq//oX2j7/gnmbG7hzVTNtf3mGmuf+D1HfxoE0FZMnX9YrdTYzKI1Joe6I\n6LLCFZS2VZDoN4prExfjKVOyvHgltR31p/Xd/Zg9moPs1uwnWh3BdUmLmRo+gRjvKA5qj1DWVnnS\n46mUcrJGBWK02KlsPCaqE3TVErwnuRt7az9Yiuh0onn3LRwtLSgWXEquNIyv1OORePug+3oVnfXD\ne53dlDVp2VZccErH7i/UorO04ZmcQ4fdyNbGLUyfKWKyOnhh+WE6zHYikptp6zSwkBTa3/sABIHI\nhx/pFZVQyVVcHDsXs8PC99V9I5aNJi1v536IS3QR4x2JU3QiDRbJ80kAg56O7AOnfP1niwZjI5tq\nthOg9Gdh/IKez9furkart1BZ7IlMIjsjBqcoimys3oqAQIgqiOr2WjpsxhE/79lGFEWO6oq6WoNE\nnvZ4o3zjcLgc1HaMTKnIiVhftYkmSwtzomeQGpDE+JAsbk+/kX/NfJq/T/sDY7r6YA7E4oSFxHhH\nsbfx4IDRIVEU2VLzA2/lftgT8SsfIKpb3fU9/DjCGRWiRi6TMHd8JJZOB7vyNGw70oAgnFgsSBRF\nmj/7FHN+rju6mTKwET1czI2eiVLqwaaa7T2id8ONh0LKpJQQdO2dFFYPvjYPXHwVnimpmHKOoP9u\n/YjM52whiuKAgRu7y8F7+R/3KEX/cfIjXBQ7t0/Npp+HL2OC01GcYi/V/vhZG5yiKLKpZjsCAvOi\nZ1FQ6a4fzIgfWlTD0xUIciva9p+X0dnUpQColnshiiJr91QBsGhaLDdd6PYwf7KxBJdLpLjGbXgm\nRw9uxMeGeqMyu6XGd9WPTFptbUcDrxx+B7Pdws0p1zIj4phQxr5CLQVVenLyHPgqfMhvKTxhT05R\nFPmqbC0KqYL7s+7AR6Hmi21ltBisfLu7ij+8tZv1+6opLRUQnYohRzc7a2uoefYZSu+7i9K776Ds\n/nsI/ddH/PrrFm5a30rs8m00vfMW2g+X0vrcX1msqKOxxdgTlRsOCjS1oGpD7YzA1+OYyIxEkHBT\nyjW8OPv/uDzhEjykCswlxWif/xdXl3phsRgJind7+M+3KGd3ymmkOpzWdisBPh4nVfMYHaLG0uli\ndsBCotQR7GrYz0dHPwPA2RzF6OMacM+IdUcuK03H+nFml2gxhx5AkLi4KeUa1Ar3/dX06cdUPvkE\nLau+xK5txGvceEJvv5PAK6/GkBiOS3Rhb2hAGyCjdko8Gf144K9JupzxIVksjJvP36b+gUfG38ec\n6BnclnYDdpeDd/OWYbSfmoOg2ayjyew2iMx2MzXtdawoXoWnzJO7Mm9FLpUjESRcl7QYgC9Lvzml\nLIasLrXCvHJdz2eCRELor+9COTqRjv37qPnH3zAX5OOVmUVjxkwATBIPOi9eguhw0Lj0nRGJ5L15\n6GO+qP+IT7I3ndRxLlHk2z2VeIzOwS6YmR05HQ+pgkPWTSQmQlObBaR2tPJcgqxyEr7ej+hwEH7/\nQ6j6WSReEDUdX4UPG2u28VL2m+zTZGNz2mjrNPDakaWYHRZuTrmGWZFuUROnh54DfmmIgoD+uw3n\nlXKjS3TxWfFXuEQX1yUt7mkyrzNY2XrYvUjXNNtI8k2k0aQd8R6PxfoyajrqGROcwfTwyYiIPZG/\n8xmX6OKFg6+xvHhVv9vrjBoMtg7SApNPK7rZTYJvHHB26jhrO+rZVLOdQKU/l4/qLaAjlUgJ8uxd\nB293OHE4ez/LZBIZv06/GaXUg+XFq3oc9N04XA4+LVrJyrJv8VGo+e24e5EK0gGvtzvC2S1kWNts\nRCaVEBbgTk2eMy4SmVTCN7uqqNS0kzkq8ISOUt3qr2jbsglFZBQR9z14yrX9J4OXXMWcqBl02Izs\nbNg3YueZmeVeZ+3KHbzcTZBICL/nfqR+frSs+hL9pu9x2UYu3fdMIIoieS1H+cf+l/jTrn/06YFr\ndzl4L28ZeS2FpPgn8sj4+wj3Glgcdbj52fbh7L7p640aJoSMIUDpz9GqQtSecqJD+yqp9kegPAQz\n9RRoqwj1GZqIxvmOw+WgxdpKnE8MACW1bZTXtzN2dBBRXQq0MzLC2JXfyNbD9ZTUthEa4FbSHAxB\nEBgflchuyyFyWvIx282o5MOnWlzX0cCrh9/pWXBNi5jUs83pcvHNTnfUpdPuIt4rkSP6bMoNVb16\nW/0YrbmJtk4DE0LGEKEOI79SR43WyLjEIEZF+LB+bw1fbHVHaxUyCZNTB7+xRaeT1vVr0a1ZDU4n\nHjGxSJRKBLkcQS5HolAgeiopsWkosTUgdwlML7KTmLeZWzyC2LcV0o/z8p8OP1S7ox0p3v339ZNK\npIguF/rvNtDy1ZfgchFUBbfkSzk4ayce8nnsyNUwf0J0n96r5yoNxkYkggQ/WQAmq4O4IfT/Op6p\n6WHsL2zi4/XlPHj9Lbx0+HVaLDpEmych8mi8Vce+h1EhIXDAF5OnFqvDilKmZFXJeiTqDsYGjOuR\nv2/bvJG2LZtRhEfgf/ElqMdPQKo6FiX3WbiQ5w++QnuLhk65wM3x/ae0qeVe3JlxS5/PM4PSuDRu\nAeuqNvFhwXIeGPPrk1o0HmrKZWn+//rddmfGLQR5HnPexfvGMjlsPPsbD7FHc6CXw2copMUFIJUI\n5JbruHLWsToqiVxB5IO/oea5f9BZW4s8OISwu+5l175jC46jykhmTZ9J++6dtK77lsArruzvFKdE\nu9WIRd6EAOxq+x7vQgVXpM4e0rFHSlvQqQ8h89YzIWQM1yUtJjUgkXfyltERugeVZjJCcDVOm4Vr\ndjtxGY2E3HQL6qwx/Y6nkCq4N+s2vinfQJG+lHJDJV+UrsZT5om+s43LR13C1PCJPaImRqGFNnkI\nLeFJBNcUY8rLQZ3Vv/L4ucY+TTblhkrGBGeQGZTW8/nqXZU4nCIxoWpqtEYCxXigkMNNuUSoT6/H\n4GBsrN4GwEWxc/CQKvi6fB35uiKmhk8csXOeCWo76qlqr6Gmo47L4i/qVVMOx9Jph6v3ZEK3cJCh\niguHZcSh4XQ5+aTQXQt8Y/KSHgfGQLhEkT+/u4/Rkb7cc0XvqGewKpCbUpbwfsGnvJ//CRfGzkFj\n0qIxNlLTUY++s41o70juzbwNf6UfMd5RVHfU0um09TqvS3RR01FHiGcQKrknTpeL+mYTkUFePSmz\nPioF09JD2dFlYM0ZO3iUufW79bR++w3y4BCifvc4UvXQ1rvDwdyYWWyt28nG6m3MjJg6In3XR0f6\nEhqgIrukGbPVjmqQnuAyHx8i7n2Quv/+h+bPPqV17Rr8FlyE39x5vd6z5wM1HXV8VbqWkrZyBARk\nEikfHf2MPQ0HuD75KgKV/ryb/zEFuiJSA5K4J/O2M973/mcZ4TTbzbx25L2eflDXJi1GozN3KX/5\nIzmBt0cURURRJMLLnbZQ8TNSqm2x6HCJrh7BoLV73LWbi6bF9uyzZE4CSoWUFVtKsdqcA6rT/pjx\nSSE4W8Nwik4q24f3O11V9i0mh5lbUq/tZWwC7C3QotVbeoxiVaf7gZ3bMniKXGFXv62UAHdUd/1e\ndz3eFTPiWTQtjn/dP41Lp8aikEmYNSZi0Lpgm6aB2uf/ie7rVUi9vYl85FFin/470b9/kqjfPU7k\nQ78l/J77ibj1Dubc+SemX/sguek+LL8iBNWkiUR2tjBt9yfUrOzfC30yiKJImekoolPKrLj+8/ed\nRqNbCXPl50h9fIh85DH8LrwYX6OTBetquN6xE6vByP/7/MiICBoNN6IoojE1EqoKpt3knm+gz8nJ\nf48dHcScsRHUNRvZvKeVuzJuRS7IsWtiSYrq7ZASBIEAYkAQyW44ys6KfNq9ipA51NyacTUApqMF\nNH/+GVJfXyIffQLfmbP7vATlUjm3p91Ap0qOrzqAiQO0KRqMhfELSA9MobC1hLUV3w/5OKfLyTfl\n65EKUqaFT2Ji6FjGBKWTGpDETSlLehkB3SxOWIhCquCb8g2Y7SenjOrpISMxypeqxg4Mpt6eaPc9\n8xg+s2YT8fAjSL28qO7q4SmVCBTXtBF8w03IAgLQrV1DZ21Nf6c4JXbW5CAIoOiIBaec7xq+ZXPl\n3hMeJ4oiX+RsQxZaQ7DSLcwlCAJZwelcNupiDDYD4RMLkARXcul+Cx6NrfjOvgDfufMHHTfWJ5qH\nx93N36f9kUti56GQKGi16pkVOY2LY+cCEOEVikwio7lTiyDAkYgJIAjovlp5XtQzGW0mvipfi4dU\nwbWJV/R8rtGZ2JWnISLIi1sudNcxW5oCkY9wWm1NRx1F+lKS/BKI9YkmVBVCoDKAQl3JCTNlznWO\n6ooBt/FzQHu4z/YCXRECAqld78HTxc/Dl0ClPxWGqjMacd9c+wO1xgamhE3oqQUejNZ2Ky0GKznl\nLf3WC04IHcuMiMnUGRv4oOBTNlRtJqelAJvTxtTwifxu/P34K91rowS/OFyiiypD7+dSs0WHxWEl\nxscd3dS2WnA4XUSF9H4PXDjRnW7r7+1BZkL//eMBDDu20/LFCqR+fkQ99gQyv6GtzYYLtdyLC6Jm\n0G7rYHfDyGSyCYLAzMww7A4X+wtPrLHimZhI/HMvELDockSHA91XK6n8/WO0nidpti7RxadFK/nX\ngVcoaSsnPTCFP03+HU9NeZzMoFRK2sp5dv//498HX6VAV0RaYDL3ngVjE87jCOe2ul0UtZZye9oN\nffohDUaLRccbOR+gNTcxNjiD29JuQCFVsLfSbeCkxw18swJ0HMqmefn/UE+YSOL46eyrgwbz8Elg\nnyxN5mZUMhVqxZnxxmiPU6itbuwgv7KVlBg/EiKPpQv6qT24YkY8n291pwueSDCom+RoP+Q29771\nHQ2kH9d78nTodNooa6skxjuyj7fZ4XSxZlcVUonAXYtSeeGzIxi03ij9PMhtPsqS0ZcPmG5S2CUs\nlBqQSKWmncJqPenxAcSGuXtTeinlXDMngatnj4IBfBguux399xto/fYbRLsd7ynTCLnpFqReg/89\nM4JSuThuHmsqvmP/vDhiojIR16xAuv4bTMmJPe0PToXK9mo6JR3QGsmo0AAchjaslZU4TUZcJhNO\nk4n2vbtx6HSoUtMJu/teZD4+eGVkYs9MpPr9N4koLeM30mpKmiNY1VbBNXdfhscZ9KSeLK1WPVZn\nJxFeYei6VJYDvIf+XOnm+vmJlNQZ2JRdR3p8Fgu97mGFtpykSX1l9JP9kthjy2N3Xbb7GSIILIq8\nEqXMA1tTE5q33kCQSIh44GHk/gNnUER7R/LYhAfwlCmH1Ofyx0gECben3cC/Dr7KhuotiMCi+AtP\nONZezUGaLTpmR07j+uSrhnQuPw9fFsbOZ3XFet4v+IS7Mm5FKRu6YZ+VEERRTRv5Fbo+vV4VISGE\n3fZrwG3M1Wg7CPH3xEeloKKhHZtUQeivbqf+vy+hXfYh0U/+pZegzqlypCkfgHmRF2C2dbLduIpV\nFatQyqXMiJo04HHbS45i8M9G6lLwwNjbUUjkmEuKkSiVXBQ1mwajhuymHCblm4ivMuGZmETITbcO\nOf0tyDOAyxMu4dL4C2k0NxHhFdZzrFQiJVIdTl1HAwG+4ylzKLh6ylQ69u6h48B+fKZMPcHoZw+n\ny8mKkq8w2c0sGX1Zz6Id4KsfKhBFuGrWKOIjvFEqpJTUmEiflsKR5nwajI0jEuXcVL0dgAtj5wDu\nRW9GUCrb63ZRbqgkyX9kFEDPBEdbixEQkAgS9moOMjdqZs/vyGQ3U2moJt43Fq9hzEga5RvHAe1h\ntOZmwrxChm3cgTDZzayr3Ii3XM2SxKEJ6Ghb3Q4zS6cTjc5MZFDfd/Y1iYvx9/BDKVMS7hVKuFcY\nPgp1n3s4wTeOTWyn3FBJcsCx30p3Om13Rlldc5dg0I/6mUeFqLlzUSrBfp4DigVZqyrRLvsQiVpN\n1KNPIA86O31O50XPYlvtTr6v3sqMiMnIR8DwmZ4RzqofKtiZp2HOuBPXFct8fAi6agn+l1yKYftW\n9N9voOWLFQhSGf4LzmSc/eSpaq9lV8M+wlQhXJu0mJSAxJ5t92XdQU5zAV+UrKbB1EhGYAp3Zf4K\n+RB7oQ4352WEs1Rfzpcl35DXcpSVpWuGfFxNex0vHHwNrbmJ+TGzuTPjlp6C2IIqd/1menz/BqfT\nbKZx6bto3ngVh15P2+ZNxDgciHY5ekdzv8eMND/U7eaZvf/hz7v+wYcFn1FhqB5xj+DxgkFru5Rp\nLz0uutnNgolRhAaoEIDkmKGlG8ukEpJD3GOVtQ5fhLNUX45TdPZEIo9nT34jTW0WZo+NICXWH2+V\nnPI6I2mByeisrTSY+q/7sbsclOrLCVOF4K/061HpvXRKTJ99JRKh36i56WgB1X97Ct1XK5F4ehJ+\n/4OE333vCY3NbuZHzyZQGcDWup2Ez05mw6iLcSGgXfYhLqv1xAMMwK46d8/PMEkirvY2qp76Mw2v\nvYz2g6U0f/4ZrWvX4GhtJXDxVUT+7jFkPsdSTyPTJpJ383R2jvVC6qcm1VjNpIINVDz6W+rfeO2c\nlSBv6BEMCqe13S1n73+SEU4AD7mUe69IRyaVsHRtIYdL3fWGiVHuRbG9pRmbxu2gGh+ViGhXUGUt\nxSYx4dGazIK0TFxWCw2vvYzLbCLkll8NSa4+1ie6V1/ck0UlV3Ff1u0EKgP4rnoLLx16kxaLbsD9\n7U4766o2IZfIuSRu8Ijbj5kbM4u0wGQKW0v47+G3MHR2nPigLrrrOHPLB54bgK7disnqICbUm+QY\nP1yiSFm9Aa+MLLwnT8VaWUHbti2DjjEUbE47ms5qXBYvsqJiuW7aeNIclyA65Xxa/AXZ2px+j2vv\n7GBV9RcguLgy9mqCFX40Ln2Hun8/R80zf6X8tw+yYF01V+UITM81IQsIIPz+hxBkJ79Q6DYuf7zI\njfGOwik68QvqxGC04X3pYpBK0X29akh1rrbmJoyHD53RiKjRbuK1nKUcasol1ieaC6Jm9Gyramzn\nYHEz8eE+jE8KQiqRkBTth7bVTLK3O9o+ElHOZrOOQ025RKkjekX5MrpSTPMGUB89HzDbzVQaaoj3\njSEjKJV6o4Y64zEHe2FrCSLisKXTdnMsrbYLzA8+AAAgAElEQVSvwFi3+vdwUmGowu5yMCty6pAN\n58ZWc8+/y+sN/e6jkMpZGL+AudEzSQlIxNfDu1+H0agB6lZr2rsFg7rqN49TqP0xMzLDSRokm6x1\n/VoQRcLvvg+PiNMXdzpVvBVqZkdNx2Br56VDb/B99Va0pqF1eyjVlw9Ja8Df24P0+AAqGtpPSrxQ\n6ulJwCWXEvOnp5D6+tK84tNzXkytu13eJXHzexmb3YwJTuepqY9zf9Yd3H0WjU04RYPT5XLx9NNP\nc/3113PrrbdSXd27P+CWLVtYsmQJ119/PZ9//vmQxhzqA8RsN/PR0RUIgkCQZyC7NQfIaT6xOqDW\n3MzrOUsx2c1cn3QVV4++rKdWyeF0UVzj7hUZ4NM3qmEuKqT6b0/RvmcXHrFxBF9/I4gi4uZ1iGZf\nbJIOzHZzn+NGCpfo4svSb1hR8jVechWBngEc0B7ixezXef7Ay6yt3Mi3Fd+xsnQNy4tW8nnJ1+gs\nwyMo021wSu3eZBc1ERvq3W9UWCaV8Oh1Y3j0+rEnrN88noSgMESnlHrj8PU3LepKff1xyo/D6WLN\n7ipkUgmLpsYiCAKjI33Rd3SS4OXeN7e5f7XaSkM1Nped1IAkNDoTh4qbiQ/3JiX2xMa1w9CG5p03\nqX/pBexNWvzmLSDuH8/hPWHgaEh/yKVyliRehkt08VX5t4SljWavfzqOVh0tX608qbF65uZycLg5\nF9HmQWZQEk3LP8FlNuE7Zx6ht99JxIO/Ier3TxL/r/8QePnifiNE8+PnkZ3mxXfXpxL2p6cpjJtM\nq0yN6dBBdGu/OaV5jTT1PYJBYT19ZAP7eRYMhegQNdfNTcBosVNS24afWkGQrxKnyUTNP5+h+u9P\nYzpawKgIH1yGIABcRl8Wxs1DcDrRvPcOtoZ6/OZfiO/ModUCDgfhXqE8OfkRJoWOo6q9huf2/3dA\nlcUdDXtp6zS4RWo8Tq7WVS6RcV/m7UwPn0xtRz0vZr9G4xAXHOGBKoJ8leRXtuIcxNCpbnQvzGJD\n1SR3ZVgU1bifgcHX34hEpUK36kvs+tN7Lpboy3AJDmgPJTLYC0EQuPeiGYTq5yC6pHxYsJxSfUWv\nY5wuJ69mL8MpMxNsGcOciGTq/t9/6Ni7B4+4eHxmzUYeGIS1tJSYAi2CQkHEQ7/t5dgZDrpFSBS+\n7gVZm0yN7+wLsDc3Ydi1o99jHG1t6Dd+R80/n6Hqyd/T8PorNH3y8RlJfWwwNvLCgVd7+h3/Zuzd\nvaLwK7e7v+clF4zqWdR3/+3pCOlKq80Z9rlurNmGiMiFMRf0MiYS/UahkMjPa+GgIn0ZIiJpAclM\nDZsAuDMbuhnu+s1uug2wirbea0uz3cwze//D/zv0JlbHqTtVf0x3Kmu8b1/n+UBojzM4Kxr6NziH\nilrhRZgqhMr26l4p2FXttUgECVFqd/nW8Qq1J4NN24jxUDYecfGo0gZX2T0TXBQ7l7TAZOqMGlaX\nr+eZff/hmb3/4WBj35TtbjQmLf89/Dbv5H40pHu4W6RxV97JryXlQcFE/vZRBIUHje++jbmk+KTH\nOFN0G5yD/XY9pAoyglKRDWJsalvNfL61jHfXFPDiZ4d5euk+fv/mbgq7gnHDwSkZnJs2bcJms7Fi\nxQoee+wxnn/++Z5tdrud5557jvfff5+PP/6YFStW0NLSMiyTFUWR5cWr0He2sTBuPvdm3oZMIuPT\noi97NVr/MYbODl4/8h5Gu4kbk69mdtS0XtvL6w102p39Gk4d+/dR9+K/cbTpCbh8MTFP/gW/BRfh\nmZiEOecwsS3uP+DxXr+RxOro5J28ZWyt3UmYKoQnJj7MU1Me5zdj72FscCYNpkbWVW5kfdVmttTu\nYGfDPrbX7ebd/GUDNhY+GZrMzUgECdU1DkTgwklRA6Z4Bft5DhgxHoggX09cZm8MjlbsTvtpzxfc\nXliFVNHnhtyVp6HFYGXO2IgeR0N3JEpqCkUiSMhtyR9wTICUgES+21+DCCycEnvCdDen2UzdC/+i\nY/8+lPGjiPnLX90ptKdYoJ4VlE6KfyKFrSWoQ/Xs8h+D3S+Iti2bsJSXnXiAH1GsL6PTZcXZGkay\nVYMx+yDK0YmE3HQLvjNnoR43HlVSMvKAgZWcE3zjSPJLoKitjHf1G5j8m+vZOO4G2qUq9Fu34uho\nH/DYs0VDl4MjwiusJ8LZn/PpeByGNpyW/usQ50+IYkxXNC4xyg9BENCt/gpnRweiw0HD668g1lYR\naEvB2RaEUDOOGUlB1L/2CqYjh/FMSSX42uuH8QqHhqdMye3pN3Jb2g2IiHx09DM+LFiO5bjFndXR\nyXdVW1BKPXpSCE8WqUTKTSlLuCz+YnRWPS9mvz6kdimCIJCZEIil00F5/cC/o5qu+s3YUG9GR/r2\n1HECyHx9CbrmOlxWK83L+xc8GiqHu9JpQyRxyKTu16lcJuGRyy5AUj0RpyjyVu6HvRRSvy5fR4O1\nGqc+hDtGz6D2+X9iKS5CPW4C0U/8kbDbfk3cM/8k4eXXiXz0CWL+9FRPr83hJKarhYXLw210a/UW\nAhddgaBQoFuzupdio13XQsPrr1LxxO9oXrEca1UlqvQMFBGRGLZvpXXt0DONToW8lqP8J/s1Wqyt\nXBI3n7szb+1VSlNco6egspXUWH/SjnuPp3Rl15TXmsgKSkdrbiZfN3wRx++rtrKrYR/BnoGM+1HP\nXLlUTkpAElpzM03m4VkDnWm66zfTApNJD0zBW67mgPYwDpcDl+jiqK4YX4U3UeqT6y99IsK9QvGU\nKftEOL+t3IjO2kq5oYo3cz8YtvYale1ugzOuq/XIUGjUuw1OuUxCecPpv9MS/OLodNqoN7nfRU6X\nkzpjPeFeoT2ZeLVNRvzUil4CdENB//0GEEUCLl54RhRpT4SXXMWDY+7kuZlPcWvqdYwJSkdnbeWT\noi+xDbDuy+tqVVduqCK7qf/MkeMZlxiEl1LG7vzGQZ2TA6GMiSXigYcQRZGG116hs+Hslc4NRmV7\nDd4KNYHKUxcuNVvt/Oezw2zYV8OeAnfHBl17Jx5yKR6K4YuICuIpuPuee+45srKyWLRoEQCzZs1i\nxw63R7SoqIgXXniBpUuXAvDss88ybtw4Fi5cOOiYf7nzZpZt3DnoPkETIhl9wxjaK1s5+tY+cImE\nzYwjbnEa+qNaij/o28Bc6iEj7f4peEX6UvtdCfWb+i7Cw9IXEZp2MZU736Fdc8zA8JJKeTE1Aw+J\nhH+WlVBmPhaaT/ZS87ekFKo8Vay+0ouadWU0bCsddP6ni0wlJ+XuyaijfDGUtlCy7BBOa28jUu7j\ngVe4D067E5fN/V/E3FEET4yifks5tetPz1Mz4W8LcJhsNG8MIjhxDiUb/42l7eT7ZUmAcT6+zAwI\nxOBwsFLTQIfTgSogjrRfX4IstIa8/+7ENMiicigofJWM/8s89EebKP7gmGdWkMhIueQvyJRqCtc9\ng8PqPo8qII7E+Y/SUrod70m1+CUFcfi5rXS29jYsMn4zA1W4N0ee3UvyhU9jM+so3vAsMPDtJACP\njxrNeF8/NjRpWVZfO8jeQ0cZ4kXWo7OwG2y4yi/Ht2In9zurqbNYeLL4KI6TuMUj5iUQszAZ19EM\nbt2/AT+ZjCeLj1J3kim6EoWUhGszCRwbga2jk4b1Bi71m8SFLQdY3ajhM83I9EM8VbIem4XCT8nB\npzYyavYDeIemkLvqMcQBXn4Bcjn/Tkmn0+Xi/7N33vFtlWf7/56jYVuSLUvy3itxbCdxdkI2GRAS\nQiDsUXZbKG0pLbyFDlqgZYcyy2wYLRAoJCEJgRACIWQnTmLH8Yj3XpKHZMna5/eHbMWOR+yMkr6/\n9/p8/Id1js6QdJ7nue/7uq/rkZIimgeQU5cpNcROvBJT6fcYrA08MSaTJoeDjxrq+EVSCl0eD6/J\nE3GnLaTr+DZusR8nRaXmcEcHL1SW4fiBxVsCDCpG3TABTUIodpON0g+O0Fnd7v+N1Hx1nLqtI09q\nnIywybGkXD0Or9ND7rM7cHUH/IMhOCqTlDl30VS4lcb8gQOdpFk/QRszlmMbfofb0Unahfeh0ieQ\n/9mDeN0OBODhUemM0QTzbFkJOebTqFAIMPnhi5AHqIj62EpaRznV9i4qbDYqbDak2IkkLZmPMvUo\njrYu8l/eTUiKnlE3TsTbpSbwcw/XW8sIVSjY3NzEv87SeDDsyxcFpv7lIhytHqi6jPrc9bQc/4br\nomNZERXN+3U1bG5u4pKISK6KiiFQJqPUamVHq5F97W3Y5CoSEydzr9xIuDKAN6or+dZ09gMrVUww\n4+6djdftpeyjXFrz+rc5JM64jdD4iZRsew5ba2Xvu2TsiidwO21UHXqO8b+eg63RwtHndw41VA8L\ncYtHEXfRKBxtXRS8vg+HqT/LKWJaPClXj6PyswIad1b2P8h5AEEuIg+U4+rsP4ZN+sMCBJlAzqPb\nQIKES8cQMy+F4ndzcHbYGffLWTTvq6H8k6MDHPnMkH77FHQZEeQ88jWuTieq6GDG/Wo2dpMNW70Z\nQ3Y07UXNFL9zCMlzBmOlAFMeXYyrw0HuszuG/bYxlzyMKFPgsDSjDk8lf/2DeM+g6tqzvq1cf4zG\nXVWoYoIZf98cmvZVU/FJPjKFirGXP4m5oYCKna8N+7hauZwXs8bT5nLy64J8zldJsPhL0oldkErR\n6oO0DyD2k3n3DIKTdEgeLy6rk9ynd+B1DS3IFTvxKsLS5lL+/WtYGofnrX4y5uoN3J2YTIvTwdNl\nJSNeC51L9KxvW/MbOf7uwGyk4SBh2s3oEqfQXLwNY+kO3HYL0iAFqurqqgFfHw5OK3Tt7OxE00sA\nRCaT4Xa7kcvldHZ2Ehwc7N+mVqvp7Dy1+fESo529i9Ip/eb4gNsD9CqSr8jCbXdR/lGerzQrCjTv\nqUKXGYEuM5LIGfG07D8R/AgygdG3TEIdq6V5bzUN35Qhiv2zO8FR6UheDzZTaZ/tN8TGoVUo+KC+\nlnK7rc+2ki4rh83tTAQSGhS0JYUjime++BoKsQtT0cRpaTlQQ+XaY0heqd/9eDqdmEv6TvpVnxUQ\nnKQjZn4K5hIjlvLTK5HLVQoUaiWdVW0og3x9ZR6HecDPdDDoFQrm68OYrw/DoDyRpZup07OmoZZd\ntja8tm7RnVgtXQ3D7+8aCKHpvr42c4mxz3UaUmehVOsxlmzH67T4tznMtXg9LtRhKZhy9hI6OozY\nhWlUfnoiESFXKVDHhmCpaCUseR6iTI7x+Df42KWDfxbXRMUwSRvKUYuZ9xtqEURhiL2HD6fRRvPu\nKqLmJOPoOE5L5zi2Hj3A4rAIroiK5tOm4VNK1FE+2t6M0iLClErWNzVQ73SM6DsGwO2l7MNcOms6\niF+aTuJV4RyrUjDdKOOi8Ag2G5vo9Jwf6o2iQkZguBprbQeiKKBU6XA7OhEkN8Ig931nQhJquRw1\n8Pu0dB4rK6bV1Tc4ldxWag+8B8CtqaMRBYH36mvIs5h5o6aSuxOSudNdzfraPSx1VxKhUrPdZGR1\nbRUeGPlnfpbhauui8NW9xC5OI3p+Klk/m0H9t+VEzkrEZXXStLPqrFxj6+F6ZEoZySvHkrJyLCXv\nDT1x2kyleD0uQmKyaC7YNOA+Kl0cLls7XpcVURSwGktRhyWjCU+hs8lHA/xHbTVPjM7gtvhECouP\nYR9hgK+O16LQyNGVq7mKGuShOqZwIstsdtvJPVxIPomQWkXmT6aj1AWhtMKM7yQmOqvxyuW8V1fN\nFmPLWRsPRgJbgwVVbAiOai8BweGIosAmYxOLwsJZERnNbL2BxCAVZreLt6ur2dl2Yu5ImnYjmsgM\nXj38AfeJdu6MT8TicXPodIL3IRA5LR5BFChfk0v7saZ+vzmZUkVIzDjsHfXY2/v/Jq3GMkJixuHt\nVGI6Uk/YpFjCsqMHDFyHi7hL0omZn4LdZKPozf242rr851WqwzCkzaMxfwMdx30tKLqMCJp3n/5C\n7VxBVMrIvOcClKGBHH1uJ66OE4vpoCgNSm0gxsP1Pg0CAUyH6omZl0LE1Dhs3cngjuKWczJWdVa1\nocuIICRZT9uxJpIuz0IQBao3FGAuMyFTygjNiGDUTRMoff8IeE8vgxAUqUEeqKAtv/9vazAIogyl\nWo/VWI6ttRJNxCjUYYlYmwdevw4H1iof0yA4RU/znmqCu+ngtlrfGkul89FqHeb6EX3eF0dEohRF\nNrc0gyict8ItHYXNxC5IRT82EnNxX10UWZCc4MRQOmvasZSZiFmQRuyCVOq2Dl3kaavaR1jaXAyp\nM7E2nx6zYWd7K3qlkmujY/lreiYfNtTylfGH0W05GSHJvvmms7r9tJ9BbfxkdIlTsJkqaTq2CSQv\nAgy69jkTnHaFMzs7m6VLlwIwd+5cduzwZYaKiopYtWoVb775JuCrcE6aNIklS5YMejyAXSuu5PPZ\nIcy55PZ+0v4WZyd/z11NtaWWWzOvZ2pUX6uGNns7f93/NzxeNxMjxuP2unF53bTa26jtrGd8WBY/\nHvejAf3lOrtc3PvC96TFaXnopsn+17tKSqh56q8oY+NI/OOfBxRrcNRUU/XIwzTpFHxxaQqPz3to\nGJ/e6cHj9fD7XX/FK3n56+w/jLjxt6KjiucOvYpWGcLvpv3qtDwuy9oree7Q31mYMJfC3VFUNlp4\n/YH5p7SR6YHteDF1zz2D5HYjBgYSPGMm2jlz6TpejHH9OiSHncDUVF5XxNM+qYD5cbO4utss/nSx\nOv99cppz+eP0+/1qd1a7iwdf24NXknjipxcQchI95fF/5VBW18GL985m1ZEXaeky8sfp9xOh8vXb\n5TQdYfWxD7gsZQlfbw7E7nTz3M9no5APPpRb9u+j4Y1XUUREkvD7h4ctDDRc2FxdPLL3aWxOJ9ac\n+Tx1yxQsqx7D3daGTKNBcruRPB4kjwf90ksJWzGwquifd62C2nqu/7oVZXgEiX9+DFF5Zj6ape0V\n/CP/X5idFqbvj2RG6VH0ly4n7PIrz+i4ZwubyrfwReU2LklaxLLkxdy96juiDCr+fNu0Afc379lN\n4z/eQJWRRdCoUZg2rEcRFUX8//xuwD478769NL75GuoJE4n9+b3+19u2baXlw/f9/+uXr8Bw2eXn\nBeXpZBxvK+PdgjW0O3zBxBVpy1iUMO+sHd8reXnx8BuUtJdzx9ibmHQSPfFkPPfxEfLLW3n2ZzP7\nUZ/NVie/emkn2akGVi4JJ0geSGOjxHMf5bJ0RiJXzT/hrWtcv5bWTRvQLVlK+FXXjOiaPyv7gm3l\n33DNhi4iujqJ/unPEFUqHFWV2Kur6CouxmMx4xZEiuLCODDRQ3SLizkH7KjdTpSxcUTedAtBo/oL\nPfyn8FHxOnbU7cF+7ALGhCXxwPW+ubV18yaMaz8BIGT2HMKvuraPX9+REiMvfuoT4MlI1PHz6SHU\nPvsUSBJx9/92WEJXw4Hb6+b3u/4KwOOz/jCgcvK2nFre33qcay5MY8kAom1f7a9mzTel3LEsg9Gp\nSh7d9wxhgXr+MP03I1Z1liSJT0o2sL12FxGqMO6d+FNCA/oqUK/eXMjOvAZ+sjyTGVlRPLn/eeqt\nTTw9508jUtQ/15Akibfy/8mRFl8ydXrUZG7OPEHj31q1nfVlm7k541qmR59YGz154AXqOhsIC9Rj\ntLfy9Jw/E3QO7qukrYznD7/Ogvg5JIbE8/axDxgXlsld428FfIJdr+au5nh7GVMjJ3Jz5rUj8hDu\nwe76/bxf9AnXpa9kTuzw1JnrjFb++NY+5mZHk50axktrj3LFnGSWz0oe8fl7IEkSv9/1FwD+OusP\nfFj8Kbvq9/Pg1HuJD45l68EaPvy6xP+7Gg68djvl//MbBFEk+alnEQNGLoT3n4JX8vK7XX8BCR6f\n/Yc+3+XBpiO8fewDlqdczPy42Ty69xmsbht/nH4/YUGDt21JksRf3suhssHMY3dOJ2YAJeHhovPI\nYZreWY2n04Jq7DiibrsDufY/aytzMj45voFva3dy36S7SQsd+W/P2NHFn1YfwOuV+PPtU4nUnTou\nCA8PPuU+g+G0kh2TJk3yB5hHjhxh9OgTYiypqalUVVXR3t6O0+nk4MGDTJw4sJffyUho8vDPwo8p\n7ziRCSxuLeWJ/X+j2lLLjOgp/YJNAF1gKNenr8QtedjXmENOcy55xmPUdtaToR/NbVk3DDoQFVW1\nIdFXnVZyu2n617sARP7olkGVAQPiE/BkTiSyzUVEecNZE+YZCMdMRVhcnUyJmnhaKlPJ2kQuSVpI\nm6OdNcXrTks4obclSlung1CNctjBpuT10rLmAyS3m4gbbyZl1QtE3nQzgYlJ6BZfTNJfnkAzeQr2\nsjJuLP4erdl9xsJBXslLUWsJuoBQv28owKbdlVjtbi69IKlfsAkwKlaLJPmER5YlL8Yrefmi8mv/\n9h7/zUR1CiazneSYkCGDTXt1FY3v/AMxMJCYn//yrAebACpFELNipuMVXMh0TZSZHETddifKqGhk\nmmDkhjCUMbGICgXtW7cMqGLrlby0dbWwcH8ngiQR+aNbzjjYBEgLTeb2rBsB2B8vRwwOoX3b13hs\nP7xibYvNxNbq7wgN0LIoYR5Wuxun2zuoYJC7o4PmNe8jBAQQecut6JevQHfxJbgaG6l97hk8J7E5\nvHY7Lf9egyCX+8TGekG3cDGGK65ECAgg4ke3ErbiivMy2AQYrUvloWm/YkrkBFK1ycyNnXlWjy8K\nIjeMuQqFKOfj4vWnVCIcn9KtVlveX622p38zNiKI5w79nVdy3yI1JqS7j7PvGK1fdimy0FDav902\n4t7io8YCph21EdHVScjc+QRPnYY6ayz6pZcSc9c9JD/9LBE/ugWvWsvYmmZu22BiyR4zSo8H9aVX\nkPjHP/+gwSZAfLdwkEZvpaX9RNtA6KKLMKy4grgHHiTq1jv6BJsut5c135Qg0xrRZB2iuLEOZ0Qc\n0Xfdg+TxUP/3l3F3nJ0qZ4GpmE6XlamREwcNDncebUAUBC7Iihxwe4+QW1F1G+EqAzOjp9LcZWRf\nY/8WnFNeT2sx22t3Ea2O5FcT7+4XbHolya+gXNHNzskKy8AjeShqO7cMqJHiq6pvOdKSz6jQFOI0\nMexrzPHbcAAU9Nh+neRJOSN6Cl7JS3OXkTRt8jkJNsGnvC0TZBS1lrCu9HPkopyrelmWKGUKfjr+\nFpJDEjjQdJjNFVtP6zwVPYJBIf2TFYOhRzAoUq8ipdsS7kz7OAVBICU0mQ6nBZO9lSpzLQpRToza\nF1zWnoZgUMfOHXhtVkIXLDyvg03wzQHjDJlYXJ1Umvv6keYbe8SpMgiUB3B52lLcXjfrSgdmuPRA\nEAQuvSARiROe8acLzYSJJD7yGKqssdjyj1L1pz/+4GJC5eYqREH0C8CNBF6vxFubCulyuLlh0ahh\nBZtnitMKOBcvXoxSqeS6667jiSee4KGHHmLjxo189NFHKBQKHnzwQe644w6uu+46rrzySiIjB54I\nesMhyEluEPFKXl7Pe4dmm5ENZV/y0pE3sbisXJ66lBvHXDXo+ydHZvP4rD/w6AUP8visP/LMnEd4\nft5fuSf7jiENTv12KL2EBtq2foWzrhbt3PkEpQ29INAuvxyPIDAz18rHuWvOmVrf3u7J8YKTfCRH\ngosTF5CiTSSnOXdQ9cmh0NxjiRIUTkenk9ARqM9a9u3BUV1F8IwLCL1wQb/BT6HTEXP3zwm/4Sbk\nkoeMEg+1nfVn9HnWWOqwum1k6Ef7F/LN7V1sy6nFEBLIoikDP6Rpcb4JpLS2g4kR44jVRHOg8TCN\n1iYkSaKw9ThqhQqv1VfNSojom/FxtbXReeQwxs/WUffS89Q+8ySSy0XUnT89p3Lk06MmASAz1FNS\n24EqI5Okxx73/f35MRL/8CdCF1+M127Hsn9fv/cbu0ykV1iJ7HDgHT8FVUbmWbu2FG0iMkmJR9eK\nOOtCvF1dtG/7+tRvPENUm2t5NfdtXs97F7u7f2/gJyUbcHvdrExbRqA8AFPH0B6czR/+C6/VStjK\nq1CEhfvUsq+6Bu38BThra6h55kmMn63DvH8v9uoqn1BQezu6S5ahDO/vJ2dYtpy0l14ldN78s3rf\n5wIahZrbsm7g15PvPiem0RGqMJYlX4TF1cnakqEXEuO6BZmODmCPUtUdcAbqzDg9TpptRmps1SRF\nB1PRYMHuPNGbIiqU6C9ZhuRw0Lbly2Ffa4vNhLeqlikFVmxBIURc01/gSVQoCZ13IaOefIrtyQup\nCQynSJ1IyWV3EXv5itOyODnb6LFbCAjpxGS24+7uhROVSgzLV6BK768++vXBGlqsrahGH8WjbkYe\nX8yBomY047MJW3kVno52Gt9646zYpfTMU9O6x7aTUdvcSVWjhfGpBrSageejuAgN6kA5RVU+0ahL\nkhchF+Vsrvga1wiF9PbU+ywSfpRxDdqA/pn+qkYLZquvF7KiwReAjDVkALC9ZmcfBdIfEsdMxWws\n30JogJY7xt7EyrRLAfi0ZBOSJGF3OyhrryA+OJYQZd/7nBI5AZngC/6zws6uOm1vKGVK4oNjqbc2\n0u7oYFHCPMKC+orVBcoDuWv8bRgC9XxRuY2cpiMjPk+luRqlTEm0+tTr1B70BJxROhVatU+FvKyu\n44zXf6nd6ryFrSXUWxuJ08T4Ey01zZ3IZQJR+uEFBpLbTdtXWxCUSkIvHJl11Q+F8eG+NUdvdwCv\n5KWgtYjQAK1fnGpq5ERStIkcacmnuHXoRE72qDBiw9TsK2jqk1Q7Hci1ocTe+2vCr7sRT5eNhtdf\n/cES5y6Pi1pLPXGamNOaj7/YV8XxmnYmjQ5n9vizK/o1GE4r4BRFkUcffZQ1a9bw0UcfkZqayvLl\ny7n2Wt+ku2DBAj799FPWrl3LjTfeOKxj1qmj0HRauDZiIZ0uK3/Zt4otVd9gCNTxm8k/Y3Hi/FPS\nJYKVGgxBerQBwagUQShkiiErBpIkcY1TkqEAACAASURBVKyilaAAOUnRvkHVZWzBtHE9suBgwlYO\nHuD2ICIlnkPaDEI7PYxdm8Oe0uE3nQ8XFmcnR40FxGqiiQ8+/YBFJsq4JfN6AmRKNpR/iVca2YKg\np8KpIhSPV0I3yAR/MrxOJ8Z1nyLI5YRdMTSNUjtrDm65kowqG12uLtoc7SO6xt4oHCBD+8n2Mtwe\niavmp6KQD5wxT+vOWJbUdSAKIsuSL0JC4vOKrTTZmml3dDBGN8ovUZ4Q6cs4SpKEcf2nVDxwH/Uv\nv0Drxs+w5h7xVbBu+BGaCcOr9J8uItURJAbHI2qNFNcPXB3WzpkHgkD7ju39tjVYGphyzIZbEAhd\ncXbprjJRRqQiHjGwi7KEFESNhratWwZVeT1TNNta+Ef+v3jq4IvkmwrJMx7j77mr+wSdR40F5JsK\nGR2ayqSIbABaLd0B5wAenJZDOXQePEBg2qg+E7ggCETccBMhs+firKuldeNnNL7xGtWP/om2rVuQ\nGwzolywd9FoHspb5/xUL4ucQHxzLvsYcCk2D90NF6lRE6lUUVLbR5egbNFQ1+Z5Lq/xEj96u+n2M\nSdD5/Dhr+1bftHPnIdP6qpwey/B6xo825HLxHjMC0Dz3csTAoEH3DQhUMuuGZbwfdwlbkhawaNGE\nQff9TyNK5bML8Qa1I0l9rR4GQkengw27KwhMO4pbcKCWq5Hpm/i+zEfL1F20BHX2BGyFx2jddGYW\nSDaXjaOmQqLUkYPOezu7LQ9mjRucYigKAukJOkxmOy3tXYQGaJkbewFtjnZ21u0d9vV0uqwcNRYQ\nrY4ctKKQW+rTTxAEX+LD7fGSFBLPWEMGJe3lfFD86X/EQmYotNhMvH3sA2SijJ+Mu5lgpYZ0fRrj\nwjIp66ggtyWfknafd3WmPr3f+zUKNdnhWQgI/mD6XKEnANMFhHJx4oUD7qNRqrlr/K0EyJT8s/Dj\nPlXaU6HLbafB2kRicNyI6NWNvSqcAKmxWqx2N81tZzaf9fiP7qjdjVfyktCtmuv1StQZrcQY1H41\n7FPBcnA/7lYTIbPmIAs+fRrkfxLpulEoRQV5xhNWh5XmaqwuG1mGdP96XhAErh61AgGBT0o2DLmW\nFQWBZRck4pUkvthXPeh+w4UgiugWLcZw2eV4Otpp+XjNGR/zdFBtqcMjeUZk5dODxlYb67+vQKtR\ncuslY/5jzKrzZqVjifDRGTJMAVwYPxuP5GFyRDYPTruXpBFQHUaC8nozxg47GYk6ZKKIJEk+TzGn\nk/Brru9DIxoMcplIfuosCgwpRLa6Ed74F8aWM/9R98aBpsN4JS8zzqC62YOwID2TI7Jpd3RQNgwb\ngt5osrWgkgfhtPsG5uFWONu3bcXd2kroootQGMKG3FcMCKAzOZMQu4vYZtcZ0WoLW48jIJCu8/UT\nldZ2cLComZSYEKZl9K82WQ4ewFZUSLBKSZReRVldB16vxPiwTBKC4zjUnMfX1b6Ewhj96D7WC5Ik\nYfz037Ru2ogiPBzDFVcS+6tfk7LqBVKffZ7QCxec9n2MBDOipyAI0EwZVnt/hVWFXo96fDaOygrs\nVZV9tpkPHiC008MxQyxhccPP9g4XY3S+wD+/swL9RUvw2my0bzs9GtRgcHlcfFS8jsf2reJQcx4J\nwXH8PPtOJkaMp6yjglfzVuPwOHF5XHxSshFRELl69Ar/gDuYJYrHaqX5/fcQ5HKibr29X5AoiCJR\nt95O8lOriL3vfsKvuxHthQtQjxtP1G13nvd0pvMFMlHGjWOuRhREPij+lJymXExdbQMu0mePi8Lh\n8rD1QN8FZnWjBXWgnFqbj25kCNRzpPkoibG+76Coum8Sq3eVs/Wr/lVOyevFXlGOrbAAa/5ROnOP\nwNrN6CweDuhGEzVp6H5T8LVs3HrJGO5aMRZN0NmvDp8uZKKMWE0MdrEdBM8paYGffleOJ+w4aFqZ\nED6Ou7NvA6BZlUNzu9X3HNx2J3KDAdPGz7AWnNojezAcas7D7XUzPXLSgAsit8fL3mONaIIUZKf1\nnVecjY1UPfIwLR+vQfJ6GdPjxdotzHJR4oUEyJRsqfwGxzCtNQ42HsEtebrH2IEXaLllJmSiwNQx\nEbjcXuqNVgRB4PaxN5IQHMfehoNsrjw9Zkd5RyW76w+MOFHcG26vmzeOvkuXu4vr0leS2MsG5Iq0\nZYiCyLqyzeR1e5tnGvoHnADXp1/Jbybf49dFOB14vF7+/W0pG3dX+gO4kzEhYiyBsgCuTb/cbw0y\nEGI0UdyWdQNur4fX897195qfClXmGiSkEa8xm1ptCILP+g0gJcbHdio7Qz/OWHU0ATIl9VZfsiyx\nO7HR1GbD5fYOm07r7ujwBUIyGbqLLj6ja/pPQilTkGFIp8nWQlO3N3NvOm1vJITEMT16MvXWRg4M\n4d8JMDUjgvDQQHbm1dNmGVoFfbjQX3wJAQmJmHd+jzX/7Ks0nwoVZh9FOOU04qM120rweCVuXDT6\nPzofnTcBpyzJFxSYjuRxZdpyHr3gIW7LuoEg+eCZ4zOBw+Xhrc99qlULJ/seavPunViP5qHKyCJ4\nxgVDvb0PwvVqNoTOonNSJoY2F9VPP4Grl5rf8bZSnst5lXcL1oyYUiNJEnsbDiIKIlMjz06FbHK3\nKNPBEdBPPF4PLV1GIlXhtHfLp+uGEXB6LBZaN29C1GjQL102rHMJ46cCkFFhp9ZyegGn3W2nvKOK\nhJA41AoVkiTx0Te+3svrFozqt2AwbdpAw2uvUPu3Z7Hm55EWp8Xu9FDb0unrA0i5CIA9DT5KVYZ+\nFNVNnQQoZYSFBmL8eA1tX272icf89ncYli1HPXY8cm3fHp9zjcmR2QiIyMLqKK0duDqs7aZvdny3\n3f+aJEmodx7BK8Cx2InDzqKOBNMTxgJQ76hEe+FCRLWatq+2nDVKilfy8m7hR+yo20NYkJ47xt7E\n/0z5BRmG0dyWeT0TwsdR2l7Bq7mr2Vz5NcYuE/PjZhGjOVEdaTUPXOFs3bwRT0cHhssuRxk1OP1E\nYTCgzhqLbtFiIm+8mdh7f41qzLmtAvxvQ3xwDBcnXkirvY3Vx97n4T1P8NCux3gt7x2qzSdUyBdO\njkMTpGDLgWo6u3zJFZvdTXN7F3FRgVRaaogPjmVu3AW4JQ+tsjJEoX8fJ/Sqcn7zdZ8qp9feRd2L\nz1P910epXfU0dc+vov6l54krNtIWrGSHbgpJ0f2FogbC3OwYxqUM7l/7QyEhOA4JL4Kqk9K6wRfM\nFQ1mdlcUoIgtRReg5cYxV5KsTSBBORpRbeaz/F0AyDQaon96D4gijW++jrv99LQN9jceQkAYULcB\n4Gi5CbPNxYzMyD7jlbOxgZpnnsRRU03bV1/S9M5q0uN943BR93cfrNSwIH4uFlcnHxR9Mqwgbm/D\nAURBHJTe22ZxUNVoIT0hlIzuvtHyblptgEzJ3dk+6ufmiq1+au6p4JW85LUcY1XOK6zK+TvvF/2b\n1/PeGbA9AHzMDmPX4Cr0OU251FsbmRk9tV97TqQqnHmxMzF2mdjdcIAgeeCgfY0qRRDJ2jMrBOwr\naOKLfdWs21HO797Yy8P/2M/GXRV9aI8p2iSenfso48JO3d4xLiyTFamX0OE088bR9wb1c+yNnl7B\nkd5LY1sXYdpAv3ZDDytqKG/g4UAmykgOOVGx6kkI9LCp4sJPHXBKXi+Nb7+Fx2wmbOVVA7ZynM8Y\n3/1d53X7buabCpELMn/hoDeWJi1GJsj4vGLrkGtrmSiydEYibo/EVwfOTkFIkMuJvPV2kMloeu/t\nc8bWGgwV3Vo3I61w5pYaySszkZGoY3J6+KnfcBZx3gSckaOTscoCcZf6mnANQbpzWub9+NtSmlpt\nXDQ1noxEHa62NlrWfIAYGEjkrbeN6NzjUw0gCDRMuJqqCbFo2rooeeIRqr5cx4ebVvHWrlcpay9n\nf+MhPi75bESUmtrOeuo6GxgXlkmwcvjN4kNhtC6VEGUwh1uODjsANtpb8UpeInoFnKHDoNSaPt+A\nt6sLw6WXIVMNTywnJHMMHQoVadUOGtqGT4/pjZL2crySlwy9r6p2oKiZsnozU9LD/T2a/mvc+Bmm\n9WuR6/QIokj9q68wRu5bdJZ1L8Ay9emkdD/YUaoI1LJgGkw24sPVmD76kLatW1BGxxD/wIPIQ0/f\ngPdMoVaoSFalIao6OVxb1mebx+thY9mX1MWokOsNmPftxWv3DZLWo3loWiwcjw9EqTv7JvMAcdpw\nBEcwNkUT3gAF+iVL8dqstH215awcf13p5xxuziNVm8zvpt7HpIjx/udYJsq4PesGJoSPo6S9nK+q\nviVYqWFp8uI+x2jtzn727uF0t7fR/s025Ho9oYsvOivX+n8YGsuSL+K+SXdzeepSJoSPQybIOGos\n4B/H3vePWYFKOcsuSKTL4eHLbqpUTbPvudVGWPFKXtJ1aUyPmoxMkLGv+SBJ0RoqGiz9aLiisn+V\n093eRs3TT2LLzyNoTAb65SsQllzInkladkwOYe3oeej0mgGFx/6bkNBNV1WGWPzj3UBYs70ARWou\ngiBwa9YNfqXzG8ZehuQVybPtwtldLQxKSSH86uvwWMw0vPn6iGmkxi4TZR2VjNKlogscWAly11Ff\nFWjWuBMJIGdDPTXPPImnox3D5SsJSErGvHsnsvX/Qhsko6i63X8tFyXOJ0WbyMGmI6zt7l0cDLWW\nemo668kyjOnX09iDo90CVtmpYSR3JyEqG04EICHKYO7Jvh21XMUHxZ9SYBpccESSJA40HuYv+57j\n9aPvUt5RxVhDBum6NPJNRfzt0Ku02U8kFK0uGx8Vr+PRvc/y9MEX6XL3X/xKksS2mh2IgsiSpEUD\nnveS5EWoupP86bpRI1bxHS68XomNu6uQiQI3Lh5NdqqBxlYr676v4M9vH/D3EgMjWostSpjH9KjJ\nVJlr2F47tK87nBAMGkmF02Z3Y7Y6/XRagPgIDXKZeMYVTjhBqw2UBfqV8WtbfAFnfOSp14BtW7dg\nyz+Kauw4dIv/e6qbPRhryEBAIM94jDZ7O3WdDYzSpRIo77/eNATpmB07A5O9ld0NQydxZo6NRhcc\nwPbD9f4E5ZkiMCER/SXLcLe2Yvz032flmMOBJElUdFShVQajDxz+etPl9vLhthJEQeD6Rf0LL33O\n4fXiqK87K734PThvAs7EOB1VQVHIbRZcjaeuarVZHANmqoeDvDIT3x6qIzZMzZXzUpAkiaZ338bb\n1UXY1dedkvZ5MmaOjUIpF9mR18DE23/DobEhyFs7cHzyGZPXH+XO9SbuXdvB8kNudtfsYWvV9mEf\ne0/DQWBwsaDqJou/KjNciILIpIjxWF02f5/jqdDcW6G2e1F+cg+n1+XE3d6Gq833Z68op/3bb1CE\nRxA6f/iUUkOoinxNCgFuCbFgaJ+lweDv3+wOOL87Ug/QxxIBwPjZOkyfrUMeFkb8bx8i+id3+yjV\nn7+HzmmmpHsBJggCy1N81j7jwjKpM1rxShKzGw7Qvm0rytg44h548AeXyQaYlzAdgELzCZqHJEl8\ndHw9X1Z9w2eVW9DOmYvksGPetxdJkmj9fCMAB1LCCdeeG1YBgNYbC6KH/KYSQhcsQhYSQtvWr0as\nEHoyvqnewTc13xOliuCn429BMUAT/Ymg01dpXZl2aT+FRZPZjiBAaPCJIMK0aSOSy4Xh0hWIiv/u\n4OK/BYIgkBaazOLE+fx43I/466zfMzt2BsYuEznNuf79LpwYS6hGydc5NXRYnf7+Ta/KN16NDk0l\nWKkhOzyLRmsTMQlOvJLkp1b2xokq5zZsxUVUP/4YjuoqtHPnEXff/ZjnT+TV8BIOZgQxesmttHRE\n+wOL/2YkdAsHhYTZaDDZBlyMdXa5qJLtRQywc0nyoj4S/PGhEegdY/DKu1hfvM3/eujCRajGjqOr\nuAhH1cgUIk8lFmS2OcktNRIXrvH30Dvq66l59ik8HR2EX3cjhksvI+43/0PQqNF0HtjP1c07MHfY\n/KJSSpmSu8bfRrQ6km9rd7Kl6ptBr2fvKeZhONG/OT7NQGy4GqVcpLy+b09wpDqCn46/FVEQeePo\nu2yv3dUv0HV6nLxX+BHvFHxIS5eR6VGT+f20X3N39m3ck30Hs2KmU9tZzzMHX6baXMvOur08svdp\ndtTtIVAegNVlY1v19/2ur7itlLrOBiaGj8MQNPAiVa1QsSzZl1TLDs8a9F7PFAeKmmlqtTFzbBQL\nJ8dx79XZPP+LOWSnGuhyuE9b3EUQBK5I8zGpjreVDbmvJElUmqvRB+rQBgz/OW5q6xYM6hVwymUi\nSVHB1DZbcTjPTBgqVet7thKCY/26JbXNPhZQ/CkqnPbKCoxrP0Gm1RJ1+4//K/UBNEo1qaFJVHRU\ns7fBJ5aZZRhcnOrixAUoRAVfVm7DNURVWyEXuXhawoBtGGcC/bLlKGNi6dj+DbaikXl92lw2jpmK\nqetsoMs9/HV8q72dDqeFZG3igEHjjtx61mwr6TeWf32whua2LhZMih20Wu5savJpkTx4P1UP/x7z\nnl0juqehcN78GqPDQ6gL9hnb2goLhtxXkiReXpvHUx8c9vfRDRcWm5O3NxciEwV+vDwThVyGeddO\nbPl5qDKz0M4dubecKlDBtMxIWtrtNDZJJF57M/9aqmff/Hg8C2aimTwFuSaYlKJWLtvdxcaSzcNS\niXV53RxsPEywQjNg835+hYlH3j7Ab1/bwz+/Kh4RN32Kn1abe4o9fehtidLeE3D2otQ6Gxuo+O39\nlN9/HxUP+P6q//ooeDyEXXnViBQZdcEBFIb46BMxxS3D7rHxX4vHRYGpmACZkuSQBCRJoqrRQqQu\niIhu6WdJkjB+to7WjZ+hCAsn/oGHUISFo5k4iYibbkGydnJdwzbqKk4kP0brUvnj9N+wLHkxVU0W\n4rqaiDq+D2VUNPH3/3ZAD8YfAhOjMhE8SiwBVdidvgHnm5rv2VXvU6atMteinDENRJGO77bTdbwY\ne1kp5TFKmpUGDOcw4ExUpQCQU1+AGBCAftlyJIedts2fn/Yxc5py+bR0E1plMPdMuAP1EB6zMlHG\nHWNv4pELHhxwMdtmthOqCUDWPVE7W5rp+P47FJGRhMycddrX+H84c1yUcCGiIPJl5TY/BVKpkLF8\nZhJOl5fNe6r880GbVI9MkJHSXS2YFeNLwnRpfH3ruWXGfsf3VTmXIjns1D7zJO7WVgxXXEnEj26l\nsrOOV478A5fXzR1ZN6Ls8s1VKf8LAk6fcJACIciXXBuoynmkoh5R34BG0LMksX/ycHHChUguJTsb\nv6fD4fsOBEHwqy9bDvRXxR4MkiSxv/EQClHBxO7k0MnYd6wJj1di9vhoBEHA2VBP7bNP+oLNG25C\nt8jHXJAFBRH7q9+gyswirKmMqxq+4UBenf84aoWKn0+4E32gjo3lWwYUEXJ73RxoOoxGoR5UJMfl\n9lBQ2UaUXkWkToVMFEmICqbe2D8ASQ1N4ifjbkEpU/Lv45/x97zV/s+sydbCMwdfZn/jIRKD4/nT\njAe4OfNaP+1fJsq4Pn0lV6Qtw+y08NTBF/mweC1ur5sr0pbx6AUPEazQ8E3NDizOvhZN22p8+gML\nE+YO+fnPi5vJ76bdd9ZaeE6GV5LYtLvSL+bSA1WgnPQEXyBcbzz9NotgpYbwIAOV5uohqdImeyud\nLuuI7FCgl0LtSWqxKTEheCWJysYzS56mhCYxMWI88+JPzDcNJivqQDkh6sETnp6uLhpefxW8XqLu\n+Ml5sx45HYwPy0JC8ieBhhKn0gYEMz9uFu2ODr6v2zPkcedlx6AJUrAtpxabfWQK1YNBVCiIvPUO\nEAQa//EmXWXDtz96r/Bj/p77Dx7f/zfu3/EwD+z4E08dePGUhaCe/s2B6LRbD9TwzhdFfHWghj+8\ntY+cYt/avc3iYMPuSjRBClbM6e/ZaT2WT81Tj1P5+9/SumkjXpuNkDlz0WSfvXHgvAk4ZTIRb4Iv\nyLAcGzrgzK9o9XtcbRqBt44kSbz3ZTEdVicr56aQEBmMq9VEy0fdVNpbbj9tGu/8CT5a0vbD9VwQ\nPYVfXvJ7brzxETJu+Akxd/+cpEcfJ2hMBonVnVy2q5MP8z+mqHXo6l2+sRCr28a0qEn9qC3G9i5e\n/+wYMpmAPiSAbw/V8eDre1izrYS6lk6O17Szr6CJL/dVs3ZHOR3WvkFbUkgChkA9ecZ8Pw2qNyzO\nTlpsJsxOCw6P09/AHamOoK3TF3D2iAZ5bDbqXn4Bj9mMeuIkgqfN8P1Nn4F++Qo0k6eO6LOUiSKS\nIYJGrZqERid19cOrwlpdNr6o2MYfdz9OS5eJLMMYZKIMY4cdm8NNYpSPCuV1Oml6d7Uv2AyPIO5/\nHkRhONFbFTpvPobLLkfrsrCo6HNMLSeoS1HqSBQyBbX17Sxp3guCQOTtd55XKnAyUUaUMApB4WR7\n+WHyWo6xrvRzRHcg7uY4QKKcVtTZE3BUV9H03tsAHMhSI9k0hGnPnTl5VkQakkekzOIblLVz5yPX\nG2j/dhuutpEzFso7qnivYA2BsgB+ln3HsOgloiAOaBbt9Uq0WZx9PDhbN3wGHg+GFVecFzYW/z/D\nEKRjRtRkmmwtHG7O878+JzsGQ0gg3x6uo7CqDWWAh4auBhJD4gnoFhoZrUslLFBPSWchGo1P3MU7\nAIVSO3c+stBQkMmIuvMnGJYtp8HaxMtH3sLpdXFb1g1MiBhHRXflKjnmv3dR1wOZKCNOE42VNhA8\nA/Zx7qnJRRAlJoZNGJBmOT09FqlhFB7crC89kTxSjR2HGBSE5cD+YdNqK83VtHSZyA7PInAAj8cu\nh5tth2qRiQIzMiNxNjVR8+zTeMxmIm64Cd2CvnRRMSCAmF/ci2r8BJK7GjB88U/cvXyIQwO0/HzC\nnWgUatYUr2NfQ06fa803FtLpsg44D/eguLodh8tDdtqJeSQl2heAVA2QFM8ypPO7afeRoR9NgamY\nx/c/x+cVW3n6wIvUWxuZG3sB902+u58NCPgC+UUJ87hz7E1oFGqmRk7iTzP+h0UJ81ApgliStBCH\nx8lXVd/639NgbaLAVEyqNrmPUNBAEASBWE30OWtpOny8hTqjlemZkf4EcA9iwnz/n0nACb6FeJfb\n7k+UDwQ/nXak/ZsnKdT2IO0s+XEqRDl3jr3Jz8TxeL0YO+x9zucyGal/5SXqXnqe+tdeoeGt16l7\nfhWulmZ0S5aizjx31en/BHp6dl1eF5GqcMJVQ/e+L06cT6AskC1V3w7a3wwQoJSxZHoCNoebL/ad\nmS9nbwSlpBB21TW+NoynHse0YT2SZ+hKd6O1iaPGAqLUkcyOnUGmIZ0QZTC1nfW8nvcOx4fw7fX3\nb4b0DTi3H6njw20laNVKLp2ZiM3u5pV1R3l1fT4ffH0ch9PDlfNSUAeeYIBJHg/GtZ9Q97dn6Sot\nQZWRSdSdPyFl1QtE3XL7sMRTh4vzagUVlhRHR44aiguRvN4B6QCSJLFhly9LHaYNJKeomXqjlZiw\nU/cH7s5vJOd4C6PjQ7l4WgKS2+2n0kbefFufoGOkSI4OJiFSw5ESI20WB2HBfY8lBgQQ+4tfUf/3\nl0g8ls+y7zy8wWpiQmOJVkcSpY4kUhWOxWWlydpMg7XJ39B+sjqt0+XhlXX5WO1ubr1kDDPHRrE7\nv5GNuyr46kANXw1AF2i3OLh92YkskSAITI7M5quqb8k3FTEp4oTSYr6xkDeOvodH6vvACAiEBRlo\nt1SiCpAToJD5GtTfeh1XYyO6i5cQfvV1p/0Z9oZBG0i+LpZFHcdp27sL4gfOdIMvON5S+Q27Gvbj\n9DgJkgdxceICFiX4qtW91WRdxhbq//4yjuoqAhKTiLnnlyj0/YMP/fIVVJbUEll4kLpXXkL3+wcR\nFSceUlXOd4S5Ogiet4CglNR+7/+hMTliIpuMx9jRuIOuxjaQZNiKJiKIHuQRtRS1lnDpvPlYDx/C\n1dREV0IkjeES3uMaDOcw4EyMCMVbrMccaqTd0UFogBbD8stoevdtWjdtIPJHt4zoeDvr9uKWPPw4\n62biuhkSp4v2TgdeSfILBjnq6jDv3Y0yLp7gKdPO6Nj/h7ODixIXsLcxhy8rv2FixHhEQUQuE7ls\ndhJvby6izeIgNrWLViTSdSeeS1EQmRkzjQ3lXxKf1s7xI6FUNVr6UWJFpZKEh/6I5Haj7PaP3lm/\nF7vHzk1jrvaPkxUNZkRBICHy/Ek0nQmStYlUmKuRaU2U1fWduyRJotpxHBSwIGVgSmmgUs7Y0Ink\nW2vY33SICRHjyA7PQlQo0UychHn3LuzlZQSl9hf+OBk9bSTToyb32yZJEqs/L6S5rYvFU+IJsluo\nWfU0no52wq+9ntAFA/cmigolsT/7OXseeZK4hlLKV60i7f77/erRkapw7sm+gxcOv857hR9xuOUo\n14xegT5Q57+eoVTic0tP9G96nU7sFeUkRfl+WxUNZkbH92+1CA3Q8rPs2/mudjfryzazuWIrSlHB\nLZnXDUol7g3fZzy2X2A4K3Y622p2sKNuDwvi56ALDOWbbortwoQ5pzzuuYQkSWzcXYkAXDqzf3Um\nxuBbx9WbhrbnORVStInsbzxERUfVoP6aFT2CQSOscDb28uDsc84epdoh+qAHQoPJyvd5DVwxJ8Uv\nQtQbpg47Hq9EpO4E88i0aQOdh3P67Rs0Op2wFVeM6PznIyJUYUSpI2m0Ng1Jp+2BWqFiYcIcPq/Y\nyvbanSxJGtx3dOHkOLbl1LL1QA0LJsUNS/xyONBffAmBiUk0rn4T04b1WPPziLrjp/555GT00N4v\nS7mY7F5MjgJTMa/nvcOree/wiwl3ktJtDdQb5R1VyASZv/8eYNfRBv75ZTHBKgX3Xz+R2DA1F2RF\n8fbmIg4U+QpGiZHBzBl/Yp3kbm+j4Y3X6DpejCIsnOi7fkZgUv/q59nCeVPhBEiKDqEyKBrsXTiq\nB84+FFa1UVZnZkqyluumhCEBQf7k3AAAIABJREFUm/eeOlNhtjlZs62EAKWMO5aOwZZ3hMo//R7b\nsXxUWWMJmTM0zeRUEASB+RNi8UoS3+fVD7iPGBBAzM9/iXp8NgkNTq74zkxTSzW7Gw6wtnQTr+a9\nzb8KP2Zr9XbyTT4u+OzYGX1UNCVJ4l9fHaeq0cxl4TbSdq+l6ZUXGH3kSx6IbuKnMe1crjXxI10z\nd+vq+LW2ghXmHEoOFfbr9fTTantJStd3NvL2sQ8QBYHpUZOZGDGeTEM6qVpfP5VClNNmcfgfUtP6\ntVjzclFljSXsymvO6DPsDUNIIAWqFDwiyA4NLK0vSRI5Tbn8Zd8qvq3diUoexMq0S/nLzIe4LHUJ\nKoVvgO7JMCda66l67M84qqsImT2H+Ad/N2CwCb7vM/H22zmujiewtoym1W/6m6ft9fWMqT6ITakm\n4qqrz9o9n01MTxqN16ahw9uM0+vCXjKONH0CYyNTkDwyCk0lqDLHIu9OspRP8Q1c57rCGaVXIZl9\nymgF3T6LITNno4iMpGPnDpwtzSM6XqO1Gbkg8/fqnglOFgwyfbYWJImwy1f+V/bC/G9EuMrAlMgJ\n1Fsb/SqG4Ouj76kABOh8lfLRur6JoBnRUxAFEauqHJD8PXcnQ2Ew9FkklLZXoBDlfrXULofbR6kP\nVxOgODeiKv9pTOsO7tRx9ZQ3mPuItlQ0m/CoWgj06IlQD65vMCMjGmf5OARkfFj0KZ1OX5UqeKqP\nzmzZf2pardVlY3/jIQyBOsboR/XbvmlPFTnHW0iPD+WKiXpqn30Kd6uJsJVXnVIgRZDLCbn5xxxX\nx0NFCXUvPY/XcaIakhASx/9M/SWjQ1M5aizgsX2r+LxiKwWtxSQExxKrGVidWpIkcsuMBAXISY3W\nUP/yC9Q+8yQxRT56bkXD4BUvURC5MH42v53yS+bFzeSBKb8YVrDpv6cBqpAKUc7S5MW4vW6+qPwa\ns9PC/qZDhAcZhqX2erZwsKiZL/dVY7GdYFDllpmobupkakYE0Yb+RQK9NhClQjzzCmd35aenEjQQ\nKjuqkQky4jQjS1Q2tXahkIvoTlIy14cEogsOoKzePOxqvsvt5e/r8vlyXzX53aJTJ6PH2zOyO8D1\nWCxY9u5BER5B6guvkPLciyQ/tYqkvz5F3P2//V/DxJnS7Y09IXzcsPa/MH4OaoWKr6u/w+YaPGER\noJCxYnYyTreX9d+Xn5Vr7YFqTAaJf36M4OkzsJeXU/Xow1gO9hcz6nBY2N+YQ0RQWL9nMtOQzu1j\nb8LtdfPKkdVUW2r7bHd6XNR21hMXHOPXqthf2MTqzYWoAuX85toJxHYX4KINah68cRLXLxpFQqSG\nm5ekI4q+McNacIyqRx6m63gxmkmTSXj4z+c02ITzrMKZGBXMLlUU2ZZSbIWFA978hl2VjOqsZtG+\nw/C1mRWR4/jy6Fgum51MROjgvWcfbSvBandz84RgHKtfpq2wAESR0AWLMFy+8qzQR6ZnRvLRt6Xs\nyK3n0guS/F9sb4gKJTE/+4WPa384h7u2ylFcewXGZD3NthY0CjVR6kiiVBFolP0H5B37SnHu+Jp7\nrCUEl5npPGm7rvuvNzKAUUIhh953sPCu6/z3GquJJlodyTFTETZXF17J65Nc9zi4PesGv31Kbzhc\nHmwON8kxIVgO7Kd18yYU4RFE/+Tus7ooD9MGYnMZqIwJILXWjKOmhoD4E1Qgs9PCR8XrOdJyFIUo\nZ2XapcyLm4lc7P+TrmrsZELHceRr9iHJZETcfCuhc+ef+hp0Ko5OWIYqZy1xB/Yj02oJv/YG6t55\nG7nk5fiExUwIOnf9jmcCfUgggZ3JOFVHcVWno5cSuWflOL4+WEORUUeLrIUOl4WoW+/AXllBob4Y\nwSJDcgb1UWg925DLRMLEBNoppMBUxMyYqQgyGYbLrqDxzddo3fAZUXf8eFjHkiSJJlszEarws6Km\n2NsSxV5ZQeehHAJTUlFn938O/g8/HC5OXMCBxsN8WfE12WFZCIKATBS5al4Kr6zLxxHQjNwj70c3\n0gaEMC4sk9yWfOTaVnJLQ7h8TsqQ57K6bNR3NjIqNAW5KMfh8vDCJ3m43F7Gp41MXO58RnxwDInB\n8VRJtbgEK7Utnf4K3bdlhxBEiVGqoSsN41P1BHp1yJrHYIk4xpritdwx9iZUGZmIajWWg/sJv/b6\nIeeJXfX7cHldzI+b5RdM6cGRUiPrd5RjCAngp4sSaeymEOovXY5+6aXDus8xKeG8lbQQec23pBQV\nUvfS88T+4ld9Kp2/nPgT9jXmsLZ0E5srfD7BM6IHbwupN9kwdtiZOiaC9nWfYOv2HrVv2URK0lIq\nGk49nsZoorhm9OXDuofhYFrkRLZWbWdPw0GcHhdur5sL4+f0+0zPFdosDt7YeAy3R2LtjnJmZEWy\naHIcG3dVAnDpBUkDvk8UBKINauparHi90oBrqOEgRhNFgExJ+SABp9Pjoqazjvjg2AEF5gaDJEk0\nttmI1AUhDrBmTI0J4WBxC2V15n5q+ANh4+5K6rqD63qTlYn0t6lo6g44I7ornO07tiO5XIQuXIRM\nPTzl//9GXJR4IePDswZN9JyMIHkgFyVeyLrSz/no+Hpuzbx+0HX9rHFRfHWghp1HG7hoWoI/QOuB\nw+lBQiJQOfIQSaZSE/3ju1CPz6bpvXdpeO0VnJevRL9suf96dtTtxi15WJAw8DOZHZ7FLRnX8k7B\nGl4+8ha3Zd2ALiDU79HqlbykdM9vHVYnb24sIFAp49fXTujHuhFFgcVT4lk8xbd+ltxujOvX0rbl\nCxBFwq+/kdAFi86pK0gPzquAM0qvoiEkFprAVlSA/pKlfbYXH6ska/960q3VCHI5Mr2ejMY8YuRl\n7F3n4rLbBvZ5PFbRSsmhIq6zFxPzaQk2SUI1djzh11xHQMyZ0fB6IyhAzgVZUWw/XEdeuYkJvRYk\nHq8XAQFRFBDkcqLvvoe2r77EtH4t9jffIWbOXCZcez1i4OABTPnGLwnb8G8WSB5QKAiZM5fQCxei\nCI/A09GOu70dd0c7XrsDmUqFqFIhU6uxNzVTtXo1CTlbqHmxidg77vTzsqdETmBj+RYONedysOkI\nRnsrlyQtHDDYBPyCQTFeC41v/xMhIJCYn9971gc+gzYQJBlVqWGk1tZR/9rLhF9zPapx4znQdJhP\nSzdiddlI1SZxU8bVRKgG9hOSJAlLZSVXG/cj02iI+cV9BKUMvcjsjYmZMXxSfyH3dHxL+9dbcTY0\n4CkvoUQdh3rC8DPRPwQyNZPYmxuKUgrhlz8aT4hKSbRBjbfcgCzUSHFrKdMzJhOQnk7TdzsRHMGE\nagIGpPWcTSTqIml1BFLYWoLH60EmygieOo3WzZsw791NyKzZw/KubHd0YPc4iDwD8/HeaDV3VzhD\nAmn9fA0AYSuv+o8MxP+H4SNKHcGkiPHkNOeSbyr0Z4gnp0fwxM8m8+jBLxkVmjLgQnJJ0gLyWo6h\nTimh6rC+D1tjIJR3VCIhkRaajMvt4aVP8zhe086UMRGsmJ10rm7xB8Gs2GlUWWq6PXw7/AFnsbkA\nlLAgeehefIVcxpT0cL7Pc5GS0MHhlqPkNOcyJXICwZOn0LHjO7qOFw/6bHu8Hr6r3U2ATMkFMX3P\n1WCy8ubGYwTj5C5DG63PfoK7rQ3d4osxrFg57HuUy0QmZUbziX02D0SH0FV0lNq/PUvsL3/lt+0S\nBIEZ0VMYa8hgfdlmGqxNTB1kPuT/sXee4XHU1x5+Z3tXl1a9d1myLblXXLANmI7pLZQQIOTCDQkJ\nCSEJCRdSISGBEBJIKAkdDLhh3LvkIrlIVl31tlpt0Uraej+sJFuoWJZl2cZ6n0dftDOzs23mf875\nnd8BCnsq5dMdhr7xWMHX3UD9Sy9yecNW/ua9DFunc1wHrItFYq5IuJTXDr/JvqYDqCTKYSXBY83a\nPdW43F5mZoRR0WBhe2ED2wt9Bny5KSFEhQ7dFxYRpMbQaKXF3NlX1TtdRIKIWF0Mx01l2J32vhE+\nvdTa6vB4PactpzV3OOh2uAf0b/ayJC+a/JIW3txQwk/vzOsznxsMQ6OVL3YZUCskdHS5aBhCRtx0\nUs+o1+XCvPkrRAoFujnnVh59thGLxCMONnu5JGouB5uLyG86SGpAErMjBm+F8SUoE3nxg0I+2FzO\nI9efaCkrLDfy6uojaJRSfnb3tFEFnQC6GbOQR0RR96c/Yvz4QxyNDYTd+S2cIi/banehFavIseqw\nFR5EnZU9IBGXp5+Cw+PkreL3+fPBvw84fu/s2OomK26Pl6V50ad0TXc0NtLw6st0G6qQhoYRft+3\nUcSPfD18ppxXAadIJBASGUqLwZ/Q48dxthlxW6y4TEa66+pwrf6MVLcDYhKIufdepCHBtH76CZ61\na/Db8R6GzioCZ0xHGhKKNDgYsUqNpeQ49a++xb3tvkyXLDKKkBtWoc7KPsXZjI6FkyPYfKCOzQfq\nyEkMoqrRyrZD9ew+2kRihI7HbpyMIAgIIhGByy9DnTWJxtdexbJtK53HjhF66+2osib1W+R6PR5a\n3/svrg3rcIrkuBesIPPq5f2aecVK5ZAD6RVx8ezrUMNHbxJbdBDDz39K+LcfRJmUTG6oL+B87/gn\nuLxuJodMGjCb8GR6nXDjaw7idTjQ3/8A8sjIIbcfLb19hK0RMRxMaWNyaTP1f/ojLZFa1mdLcAap\nuD75ShZEzR42a2syd7LQsAWx10PYXfecVrAJkJsawrub5GzJXMnywx9hP3IYt0TG+uAZPKg/v/u3\npqWGcbDUyH1XZvRZYIcHqXBbgpDis8mfEZ5La6cRl9eNu0NN+Fl0qO0lOlRLQWUIXfIaDNZaEvxi\nEUQiwu64i5rnfk3ja38j9me/PGWzeq8hhH6IZMPp0lvhDJB56Cg6hCwyakSB7wTjz/K4xRQ0H+Lz\nyg2kBiQh6zEHaujy9WUNNiQcIEYbxQx9Lrsb8xGH1FJY3sqCyUNfv0pNPslVnDaOlz46zNEqE1OS\ng7l/Zcawi8kLkdzQybx/fDWekFpK60wsyYvG2m3HJmlA3O1HStipr/OzMvVsK2wg2DwDo7KJ/5Z8\nRLJ/AtppMzBv3YJ1354hf1MHW4po7zazIGoOSsmJ61C3083b//6KRdUHybIbcJe5EOQKAldeRdCV\nV592QmhGeiibD9SxO2M5l/qpsO7dQ+1vniPy0e/3c/bUyNTclj58y4TH62VfcTMRXS1ov9qASKUi\n4uFHkIXpCVxxOXzxGZe27KGyPo9JieNbEZ8ckkW0NpIaax3zImf1GWidbSwdDrYcrCNQJ+dbl6cj\nEgkcrjDyZX4tVY1Wrpo7vGzvZOOg0QacAAk9AWelpYbMoP4u/+XtVcDp928O5VDbS0q0P3Mm6dlR\n1Mim/XUsyRvcoMnl9vCPL47h8Xq5b2Umf/6wkAbj4DLi5vYTFU7r/nxcJhP+i5ciPk/VVecSsUjM\n3Zm38uy+P/Lu8U+I08X0a0k7mZykIFKi/DhY1srxmnaSIv34eHsln+2sAqCjy8WHWyq4Zeno23Xk\n0dHEPPkU9S+9iHX3LpwtLTQlh7KosJ64Vg+Njt8APXHJqptQZ/b3KpkdMR2tTEOJqQyH20G324HD\n7UQmlpLZ49zb0FMhjzxpzImjsZHu2moEmQyRTI5ILqerupqWd9/B292NbvZcQm+5ddgC19ngvAo4\nAWL1OgxKPSHmYip/8L/9HnOLpBxNX8z1j97alw0Ive4GyvwTcHz4NhH799Gw/4ReWqRU4unsJBbo\nCI4i+abrUGfnnNV+rJgwLQkROorKjTz9z33UNPtEryJB4EiViZLqdtJiT4he5VG+L6Rx9Se0ffEZ\ndS/8HlV6BsE33IgiJhZPVxcNf3+FjoMHMCn8+Th6Cb+8+TLE4tN7DXPnpPGDg8vJMxYxq+UA9X9+\nkbhnnydEFUSsLhqDpYYoTQR3ZNw4fABn60bwevCrPoZYqztrZiq9fYSCy58teVraJieQsK2UuDor\nt9SBPDeO0IgQBIZfbDR89BH67jbMyZNJGYU0MsRfSaxey4EmGzd/5xEsb73OLk0q1m410aHnd8A5\nOTmYP//PvH4LsvBANV67FpFHTompDK/XS4Pd1zfpsWsICjx7ctpeokM0uA8EIwmt4aixmIQea29l\nYhJBV12D8aMPaHz9NSIeemTYxWRjj3OyXjU2FU5jT8Cpqj6O2eVCm3d67soTjB8RGj15YZPJbzrI\nbwte4t6s2wlVBXO83Td7LzlgaCOvlYnLKGguxBtVyv6yzGEDzrL2SkSCiC+32igsbycrIZAHrspC\ncprX3wsBhUTONP0UdtTvobS5DMhmY2k+gshLlPTUZj8AKTH+BGjlFBV3c/11K3i/7FPeLv6ABybd\niVinw1qQT+jNtw3aZ7apZjsCAgujZvf7f/E7H7Dy8BcASMP0+C9ajG723FEvuJOj/PHTyMgvNXLL\nQ/chUigxb91MzfO/Juqxx5EGjtw8cM1uA621TdzbshXcbsK//SCyMN8CN+jKq2k5WERmfSX127dD\n4thJZkeCSBBxc+q1bKjewqLo8auGrd9Xg8PlYcWM2L7fSXZiMNkjDLh7DSDrWzuYkjz6ZGLvyIhK\ns6FfwOn1etnXdACRICJ1kD7h4ehzqB0mEL7hkiQOlrby0bYK8tJC8dcMVFB8sctATbONednhZCcG\nERaoosFox+v1DrjnNbXZ0SilqBVSqr9cD4IwpDnWBD4389vTb+BvRf/itcNv8oNpjwyabBEEgRsu\nSeJX/y7gPxtLUcolHDOYCPZTcP/KTP7xxTE2FtQyLT2U5KjRz1eX+PkR9fgPafrnP7Du3Y1feRl+\ngDg0FG3WJDxdXVh27aTuD79FlTWJkBtuRB4Z1bf/pOCMYXuvew22woNUdFUbaPt8Nbb9BTBIH7FI\nqSTs/gfQTZ856tdzJoiffvrpp8/JM38Ne09juc3uZHOFnXSti4CURFSZmWim5rFNFM3Hyhyuvm3J\ngDmB4bF6/mpQUy0JImV6FnK9HplOh8vpphx/dsbP57InHkAVFTku8jixSGB/aSsdnU6mJIdw46Ik\nLpkSxbbCBto7HMzO6p9xEUQiVOkZaKbk4jS2Yj96BPOWzTiamjCtW0NnSTHipFT+op5HUmo0MzMH\nz9gMh1QiosvpYUOzjEmJQcgqixHEIlRpGWilGqwOG9/KuhWtbPiq0uEKI7ZjR8k2laCbMwfNWepv\nk0vFfLazCp1GRIfSQLPIjjUrjszcxSha2nEUl2DZsR3r3j14HQ6koaGIFP2Dpa6qKqxv/xOLRIXs\ntvvRh566p2IwOjqdHKkyERWrZ9INV/Dq3nb8NDIumznQZe984+vfd4lYxPbCBlwyE52SVvLCJlNq\nquB4ezmuxjhyomPIiBvcSGmskErErNvRjCzcQEtXK/OjZiPuSXIok5LpPF6C/chhJDo/FPFDZ8N3\nNxZQba1ledwS/ORnHvyv3VuNvcvFPOMhnI0NhN56B5LzaNzNBP3JCk6nw2nnsPEYexsLCFeHsaPe\n51S9KuWqIRNnCokCr9dLqeU4reZOLk2bMmgCr8vVxftlq/EXhVJ6yJ/02AAeuS4b2TfEKGgw/OQ6\ndtTvweFxMDtqKh+XrsWOmWURlxEXcuqAQRAELB0OjhlMzE1MwypqoMRUxszwPKSWDjpLSlAmJSML\n7e/cWGmu5ouqDUwKTmdBlG/+YJ+y56s1WMQqVLffQ/xdd6BMTOrnGH66CIJAm6WLkup2kqL8SFg4\nC6/DQcehg9gKClBn54xoFEDJgRJK3vuEZa37UDk6CFl1M7pZJ4JlQSRCnJhK27ataGtL8Zs+fUxH\nDIwEf7kfU0Oz+xQAZxtbp5NXVh9Bo5Byz+Xpp50YB5/SbWNBLf4aObmpow84VT0GMhJBzIzwE47H\nNbY61lRtJDskk9kRp5dU3Hu0mbI6MytmxBKoGzw5K5eKUcolFJS0YLY5yE3tnxCtbbbxt9VH8dfI\n+e512UglIoqr26lusjE/JwKl/EQyxu3x8O6mcqJC1EzTdtL26SeocyYPGP0zQX/06lA6nZ0cNh7D\n3G0hJ2TwMTGBOgW1LTaOGUy0mruYkhzMY6tyCAtUERumZXthA6W1ZubnhJ+RokUQi9FMzaVe7WKz\nuhHb5fOYe8f3UU/KQTMlF/XkKTibm3vW/psQazQjlrp+sceAoqWe3NLNGN/7D46GeuSxcQQuW44q\nLR1FUjKKuHiUScno7/oWquTUUx90GNTq0bv6nocVTi2tcn/2Zl7HfSszcLrc/OOLYvZ0NZGeGDBo\npkEiFrF8ZjxvbXDymyoAfyAWwR+8fvDo9Tko5OP3Umdn6dGpZcSEavA7KbuVHhvAkco2qhotff0x\nJyOPjibqf/6XjqNHaH3vv1j3+IbY6ubNpyh1Ed2bK5iUOPrRLUtyo1i3t5oPu2O4188P0/p1+C9c\nTHZIJtlD/CC/jsnaTbrNJ08+m6MipBIRfhoZtlYpcyfNJEoTzuzw6YhFYrzTl9J5vATzti3YCvJp\n/eBdWj/+AE32ZHTz5qPOmoTX7aLxH39D8Hr5InQ234sbfRUsLy2UD7ZUkF/STEZcALZOJ6kxo894\nnWvCg9QcawtA5ldPiamMho5GALyd6rM6EqUXf40MjVyJyBRPe2AZu+r3Mr+nqiGIROjv/TaGp39C\ny3/fRpmcjDxqcFlSY0cTAgJhqrGRqpksXYSqBOyFhcgiIsa0v3uCsUcqknBT6jXE6aL5T8mHvFz4\nOgBpAcmDmoedzOKY+Wyo3E5XaCV7yiuZlzawgldp9g2OF9l93687V6R9o4NN8EmO/YQQ2v1byK+q\noNVbg6dTy4zEkY9+mpWpZ82eanYfbWbqtBzKzVWUmMqYMm0m7V9txLp3D+qs/s6Tm2u3A7Awai7g\nM7ZofOMfWHftxCT355O4ZTwzdwbCKE1kvs6M9DC+zK9lz9FmshODCb5+FSKVCuNHH1Dz3K+JevTx\nfiZ1vXjdbszbt2LauhXBUMlsAJmcwMtX4r/00gHbB8ZE8G7MXBZVbaL2t88R+chjgx73m8LGglq6\nHW6umhM/6t9KiJ8SiVhE/RAS05GikaoJVQVTZen5HfckoHbV+8bczBpFT2vfSJSg4aW+C3Ii2F7o\na6Walx1Oelwgnd0uthc2sHZvNW6PlzuXp6JS+K5TET3Hqzd29Atke0eihAaoaP/SZ2AVsGTg92yC\ngVyVdBnl5kp2N+aT4BfLnMgZg253wyVJmKzd5KWGsmx6NIIgYHfa0YdJWJwXxZf5tXyyvYrrF564\nBpa1V/JVzTZyQ7OH9Ds5GbvTTrGpjM+11TQlqrgus7+jtiImlsjHHqejqJCm11+j+Z23EGk0p6xE\ner1e3DUGbqv5gs4aL4qkZIKuWIkqc9J56T1x3gWc+kAVcqkYQ5MVc4eDP39QSHm9haRIP7595dBB\n0cIpEcgkIlrMXbTbujHbHLTbusmMD2RSwuiDtNEgCMKgz3nZrFiOGUx8vsvAQ9cMbfWszshE9dOn\nseXvw+t2oZ05m7fePQRwRq9Fp5YxLzucr/bXYc5bhGbjRxg//ZiwO+4a8THaLXZm26oR6XQok898\nFMVwBPspqGqwcmPyNf3c6gRBQJWahio1DffNt2Hds8sXfB4owHagAElAIDK9Hkd9PUeCM2gPiRtU\n1jJSwgJUxIRqOFLZRkl1OwAxw5genO/og1Qcqfd9j4pNZbTYWxEjwetQEjxE1nYsEQSBqBA1xVXR\n+AUbWGfYxKzwaX0mL9KAAPR330v9n1+g4W8vE/PkU30ukifTZG8hUOE/Jtl7p8uNxe5kurwZr8uF\nJndCTnuhMDM8j0hNBK8W/QtjVxupgaeWf8rEUhaGLWFd0yesr90waMBZ1u7r3+xo0aJWSAgZh2TM\n+UBuSC5fNa9lXdMnIPYQ4I49LeOMqFANUSFqCstbWbHAd48obitlVsbNSAICsR0owOO8s69Kaep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uG5/OWxBbz06AISI/zoqqzA0VCPevJUlEnJxDz5FKrMLDoKD2H4xVO0fvg+naXH8brdfcd0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\nDqGEU0RERERERDrE/wcSoV1Ss9ufyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1101a07d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(13,7))\n",
    "\n",
    "# Filtered states confidence intervals\n",
    "states_cov = np.diagonal(sim_res.filtered_state_cov).T\n",
    "states_upper = sim_res.filtered_state + 1.96 * states_cov**0.5\n",
    "states_lower = sim_res.filtered_state - 1.96 * states_cov**0.5\n",
    "\n",
    "ax = axes[0]\n",
    "lines, = ax.plot(sim_res.filtered_state[0], label='Capital')\n",
    "ax.fill_between(states_lower[0], states_upper[0], color=lines.get_color(), alpha=0.2)\n",
    "\n",
    "lines, = ax.plot(sim_res.filtered_state[1], label='Technology shock')\n",
    "ax.fill_between(states_lower[1], states_upper[1], color=lines.get_color(), alpha=0.2)\n",
    "\n",
    "ax.set_xlim((0, 200))\n",
    "ax.hlines(0, 0, 200)\n",
    "\n",
    "ax.set_title('Filtered states (simulated data)')\n",
    "ax.legend(loc='lower left')\n",
    "ax.xaxis.grid()\n",
    "\n",
    "ax = axes[1]\n",
    "\n",
    "# One-step-ahead forecasts confidence intervals\n",
    "forecasts_cov = np.diagonal(sim_res.forecasts_error_cov).T\n",
    "forecasts_upper = sim_res.forecasts + 1.96 * forecasts_cov**0.5\n",
    "forecasts_lower = sim_res.forecasts - 1.96 * forecasts_cov**0.5\n",
    "\n",
    "for i in range(sim_mod.k_endog):\n",
    "    lines, = ax.plot(sim_res.forecasts[i], label=sim_mod.endog_names[i].title())\n",
    "    ax.fill_between(forecasts_lower[i], forecasts_upper[i], color=lines.get_color(), alpha=0.1)\n",
    "\n",
    "ax.set_xlim((0, 200))\n",
    "ax.hlines(0, 0, 200)\n",
    "\n",
    "ax.set_title('One-step-ahead forecasts (simulated data)')\n",
    "ax.legend(loc='lower left')\n",
    "ax.xaxis.grid()\n",
    "\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimation on observed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  Statespace Model Results                                  \n",
      "============================================================================================\n",
      "Dep. Variable:     ['output' 'labor' 'consumption']   No. Observations:                  124\n",
      "Model:                                 EstimateRBC1   Log Likelihood                1443.612\n",
      "Date:                              Fri, 22 Sep 2017   AIC                          -2875.224\n",
      "Time:                                      21:40:33   BIC                          -2858.303\n",
      "Sample:                                  04-01-1984   HQIC                         -2868.351\n",
      "                                       - 01-01-2015                                         \n",
      "Covariance Type:                                opg                                         \n",
      "================================================================================\n",
      "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "beta             0.9855      0.010    100.625      0.000       0.966       1.005\n",
      "rho              0.9715      0.016     59.107      0.000       0.939       1.004\n",
      "sigma.vareps     0.0024      0.000     10.853      0.000       0.002       0.003\n",
      "sigma2.y      9.357e-06   2.67e-06      3.509      0.000    4.13e-06    1.46e-05\n",
      "sigma2.n      2.449e-05   4.72e-06      5.184      0.000    1.52e-05    3.38e-05\n",
      "sigma2.c       1.53e-05   2.08e-06      7.368      0.000    1.12e-05    1.94e-05\n",
      "=====================================================================================\n",
      "Ljung-Box (Q):          40.37, 125.36, 48.44   Jarque-Bera (JB):    10.59, 0.19, 4.74\n",
      "Prob(Q):                    0.45, 0.00, 0.17   Prob(JB):             0.01, 0.91, 0.09\n",
      "Heteroskedasticity (H):     1.69, 0.89, 0.51   Skew:                0.26, -0.09, 0.40\n",
      "Prob(H) (two-sided):        0.10, 0.70, 0.04   Kurtosis:             4.34, 2.92, 3.53\n",
      "=====================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Setup the statespace model\n",
    "econ_mod = EstimateRBC1(\n",
    "    output=econ_observed['output'],\n",
    "    labor=econ_observed['labor'],\n",
    "    consumption=econ_observed['consumption'],\n",
    "    measurement_errors=True,\n",
    "    dates=econ_observed.index\n",
    ")\n",
    "\n",
    "econ_res = econ_mod.fit(maxiter=1000, information_matrix_type='oim')\n",
    "\n",
    "print econ_res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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qflhL9z0wVKS9IUk6OXOasBBCCCGEEEIcDYbWWh/tjThcAwMrO61hPqm5C6UVuI5Je1MC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4Ys+w/GqqSaONiv3N9CiGqnzJCxIJJtGSQ9e0FUeK1jT9hmx14IEY+i2HbzL8dFG0a5HRhV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\nRikFgDGG6667jiuvvJJoNDoi2z+eDWXfLFy4kM7OTgA6O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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff50950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1, figsize=(13,7))\n",
    "\n",
    "# Filtered states confidence intervals\n",
    "states_cov = np.diagonal(econ_res.filtered_state_cov).T\n",
    "states_upper = econ_res.filtered_state + 1.96 * states_cov**0.5\n",
    "states_lower = econ_res.filtered_state - 1.96 * states_cov**0.5\n",
    "\n",
    "ax = axes[0]\n",
    "lines, = ax.plot(dates, econ_res.filtered_state[0], label='Capital')\n",
    "ax.fill_between(dates, states_lower[0], states_upper[0], color=lines.get_color(), alpha=0.2)\n",
    "\n",
    "lines, = ax.plot(dates, econ_res.filtered_state[1], label='Technology shock')\n",
    "ax.fill_between(dates, states_lower[1], states_upper[1], color=lines.get_color(), alpha=0.2)\n",
    "\n",
    "ylim = ax.get_ylim()\n",
    "ax.fill_between(dates, ylim[0]+1e-4, ylim[1]-1e-4, rec, facecolor='k', alpha=0.1)\n",
    "\n",
    "ax.hlines(0, dates[0], dates[-1])\n",
    "\n",
    "ax.set_title('Filtered states')\n",
    "ax.legend(loc='lower left')\n",
    "ax.xaxis.grid()\n",
    "\n",
    "ax = axes[1]\n",
    "\n",
    "# One-step-ahead forecasts confidence intervals\n",
    "forecasts_cov = np.diagonal(econ_res.forecasts_error_cov).T\n",
    "forecasts_upper = econ_res.forecasts + 1.96 * forecasts_cov**0.5\n",
    "forecasts_lower = econ_res.forecasts - 1.96 * forecasts_cov**0.5\n",
    "\n",
    "for i in range(econ_mod.k_endog):\n",
    "    lines, = ax.plot(dates, econ_res.forecasts[i], label=econ_mod.endog_names[i].title())\n",
    "    ax.fill_between(dates, forecasts_lower[i], forecasts_upper[i], color=lines.get_color(), alpha=0.1)\n",
    "\n",
    "ylim = ax.get_ylim()\n",
    "ax.fill_between(dates, ylim[0]+1e-4, ylim[1]-1e-4, rec, facecolor='k', alpha=0.1)\n",
    "\n",
    "ax.hlines(0, dates[0], dates[-1])\n",
    "\n",
    "ax.set_title('One-step-ahead forecasts')\n",
    "ax.legend(loc='lower left')\n",
    "ax.xaxis.grid()\n",
    "\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "    [1] DeJong, David N., and Chetan Dave. 2011.\n",
    "        Structural Macroeconometrics. Second edition.\n",
    "        Princeton: Princeton University Press.\n",
    "    [2] Ruge-Murcia, Francisco J. 2007.\n",
    "        \"Methods to Estimate Dynamic Stochastic General Equilibrium Models.\"\n",
    "        Journal of Economic Dynamics and Control 31 (8): 2599–2636.\n"
   ]
  }
 ],
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