diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index 13c3fbfa..716decb4 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -34,54 +34,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "\n",
-       "            <div id=\"WSs79q\"></div>\n",
-       "            <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
-       "                if(!window.letsPlotCallQueue) {\n",
-       "                    window.letsPlotCallQueue = [];\n",
-       "                }; \n",
-       "                window.letsPlotCall = function(f) {\n",
-       "                    window.letsPlotCallQueue.push(f);\n",
-       "                };\n",
-       "                (function() {\n",
-       "                    var script = document.createElement(\"script\");\n",
-       "                    script.type = \"text/javascript\";\n",
-       "                    script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.4.0/js-package/distr/lets-plot.min.js\";\n",
-       "                    script.onload = function() {\n",
-       "                        window.letsPlotCall = function(f) {f();};\n",
-       "                        window.letsPlotCallQueue.forEach(function(f) {f();});\n",
-       "                        window.letsPlotCallQueue = [];\n",
-       "                        \n",
-       "                    };\n",
-       "                    script.onerror = function(event) {\n",
-       "                        window.letsPlotCall = function(f) {};    // noop\n",
-       "                        window.letsPlotCallQueue = [];\n",
-       "                        var div = document.createElement(\"div\");\n",
-       "                        div.style.color = 'darkred';\n",
-       "                        div.textContent = 'Error loading Lets-Plot JS';\n",
-       "                        document.getElementById(\"WSs79q\").appendChild(div);\n",
-       "                    };\n",
-       "                    var e = document.getElementById(\"WSs79q\");\n",
-       "                    e.appendChild(script);\n",
-       "                })()\n",
-       "            </script>\n",
-       "            "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <div id=\"8a4vyS\"></div>\n",
+       "            <div id=\"lfAkYZ\"></div>\n",
        "            <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
        "                if(!window.letsPlotCallQueue) {\n",
        "                    window.letsPlotCallQueue = [];\n",
@@ -92,7 +52,7 @@
        "                (function() {\n",
        "                    var script = document.createElement(\"script\");\n",
        "                    script.type = \"text/javascript\";\n",
-       "                    script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.4.0/js-package/distr/lets-plot.min.js\";\n",
+       "                    script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.3.3/js-package/distr/lets-plot.min.js\";\n",
        "                    script.onload = function() {\n",
        "                        window.letsPlotCall = function(f) {f();};\n",
        "                        window.letsPlotCallQueue.forEach(function(f) {f();});\n",
@@ -105,49 +65,9 @@
        "                        var div = document.createElement(\"div\");\n",
        "                        div.style.color = 'darkred';\n",
        "                        div.textContent = 'Error loading Lets-Plot JS';\n",
-       "                        document.getElementById(\"8a4vyS\").appendChild(div);\n",
+       "                        document.getElementById(\"lfAkYZ\").appendChild(div);\n",
        "                    };\n",
-       "                    var e = document.getElementById(\"8a4vyS\");\n",
-       "                    e.appendChild(script);\n",
-       "                })()\n",
-       "            </script>\n",
-       "            "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <div id=\"9idEFz\"></div>\n",
-       "            <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
-       "                if(!window.letsPlotCallQueue) {\n",
-       "                    window.letsPlotCallQueue = [];\n",
-       "                }; \n",
-       "                window.letsPlotCall = function(f) {\n",
-       "                    window.letsPlotCallQueue.push(f);\n",
-       "                };\n",
-       "                (function() {\n",
-       "                    var script = document.createElement(\"script\");\n",
-       "                    script.type = \"text/javascript\";\n",
-       "                    script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.4.0/js-package/distr/lets-plot.min.js\";\n",
-       "                    script.onload = function() {\n",
-       "                        window.letsPlotCall = function(f) {f();};\n",
-       "                        window.letsPlotCallQueue.forEach(function(f) {f();});\n",
-       "                        window.letsPlotCallQueue = [];\n",
-       "                        \n",
-       "                    };\n",
-       "                    script.onerror = function(event) {\n",
-       "                        window.letsPlotCall = function(f) {};    // noop\n",
-       "                        window.letsPlotCallQueue = [];\n",
-       "                        var div = document.createElement(\"div\");\n",
-       "                        div.style.color = 'darkred';\n",
-       "                        div.textContent = 'Error loading Lets-Plot JS';\n",
-       "                        document.getElementById(\"9idEFz\").appendChild(div);\n",
-       "                    };\n",
-       "                    var e = document.getElementById(\"9idEFz\");\n",
+       "                    var e = document.getElementById(\"lfAkYZ\");\n",
        "                    e.appendChild(script);\n",
        "                })()\n",
        "            </script>\n",
@@ -161,9 +81,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "The watermark extension is already loaded. To reload it, use:\n",
+      "  %reload_ext watermark\n",
       "pandas    : 2.2.2\n",
-      "matplotlib: 3.9.0\n",
-      "pyfixest  : 0.24.0\n",
+      "matplotlib: 3.9.1\n",
+      "pyfixest  : 0.24.1\n",
       "numpy     : 1.26.4\n",
       "\n"
      ]
@@ -190,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -318,7 +240,7 @@
        "4 -3.115669  0.790815  "
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -418,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -427,7 +349,7 @@
        "pyfixest.estimation.feols_.Feols"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -455,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -570,6 +492,229 @@
     "fit.se()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once a model has been fit, it can be used to predict on new data using the `.predict()` method. Data is passed in with the `newdata` keyword, and must have the same format as the training data. If the `newdata` argument is not passed, predictions will be generated from the training data. Predictions are available as a Pandas DataFrame in the column `yhat`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>yhat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.048071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.297045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.258643</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.640779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-0.243660</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.979697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-0.174500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.067080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.650296</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       yhat\n",
+       "0  0.048071\n",
+       "1       NaN\n",
+       "2  1.297045\n",
+       "3 -0.258643\n",
+       "4 -1.640779\n",
+       "5 -0.243660\n",
+       "6  0.979697\n",
+       "7 -0.174500\n",
+       "8  1.067080\n",
+       "9  0.650296"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data = pf.get_data(N=10, seed=1)\n",
+    "fit.predict(newdata = test_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prediction standard errors can be generated by setting the `compute_stdp` argument to `True`. This returns the standard error of the model prediction in a new column called `stdp`. Note that this is different from standard error of the forecast, as the additional variance associated with introducing an unknown data point is not included. (To get standard error of the forecast, simply add the model mean squared error to the output).\n",
+    "\n",
+    "Unfortunately `compute_stdp` does not yet support models with fixed effects, as this involves computing the full covariance matrix and is somewhat involved. We hope to incorporate this feature soon. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>yhat</th>\n",
+       "      <th>stdp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.081568</td>\n",
+       "      <td>0.004707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.918518</td>\n",
+       "      <td>0.012478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.081568</td>\n",
+       "      <td>0.004707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.081654</td>\n",
+       "      <td>0.010521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-0.081568</td>\n",
+       "      <td>0.004707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.918518</td>\n",
+       "      <td>0.012478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-0.081568</td>\n",
+       "      <td>0.004707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.918518</td>\n",
+       "      <td>0.012478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.918518</td>\n",
+       "      <td>0.012478</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       yhat      stdp\n",
+       "0 -0.081568  0.004707\n",
+       "1       NaN       NaN\n",
+       "2  0.918518  0.012478\n",
+       "3 -0.081568  0.004707\n",
+       "4 -1.081654  0.010521\n",
+       "5 -0.081568  0.004707\n",
+       "6  0.918518  0.012478\n",
+       "7 -0.081568  0.004707\n",
+       "8  0.918518  0.012478\n",
+       "9  0.918518  0.012478"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fit = pf.feols(fml=\"Y ~ X1 | 0\", data=data, vcov=\"HC1\")\n",
+    "fit.predict(newdata = test_data, compute_stdp = True)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3510,7 +3655,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -3524,10 +3669,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  },
-  "orig_nbformat": 4
+   "version": "3.10.12"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/justfile b/justfile
index 1f74e2c7..6b6ae2a4 100644
--- a/justfile
+++ b/justfile
@@ -1,5 +1,5 @@
 # uncomment for windows
-set shell := ["powershell.exe", "-c"]
+# set shell := ["powershell.exe", "-c"]
 
 # docs: justfile docs: https://just.systems/man/en/
 default:
diff --git a/poetry.lock b/poetry.lock
index 75ef31c4..aacaf2d8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -416,78 +416,78 @@ files = [
 
 [[package]]
 name = "cffi"
-version = "1.17.0"
+version = "1.17.1"
 description = "Foreign Function Interface for Python calling C code."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -998,19 +998,19 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.15.4"
+version = "3.16.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
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-typing = ["typing-extensions (>=4.8)"]
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+typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
 name = "fonttools"
@@ -1225,13 +1225,13 @@ extra = ["Pillow (>=10.2.0)", "selenium (>=4.18.1)"]
 
 [[package]]
 name = "griffe"
-version = "1.2.0"
+version = "1.3.1"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -1340,32 +1340,36 @@ files = [
 
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+version = "8.5.0"
 description = "Read metadata from Python packages"
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
-zipp = ">=0.5"
+zipp = ">=3.20"
 
 [package.extras]
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+cover = ["pytest-cov"]
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 perf = ["ipython"]
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+type = ["pytest-mypy"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.4.4"
+version = "6.4.5"
 description = "Read resources from Python packages"
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -2219,13 +2223,13 @@ traitlets = "*"
 
 [[package]]
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+version = "0.4.2"
 description = "Collection of plugins for markdown-it-py"
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -2287,101 +2291,103 @@ typing = ["pandas-stubs", "pyright (==1.1.364)"]
 
 [[package]]
 name = "multidict"
-version = "6.0.5"
+version = "6.1.0"
 description = "multidict implementation"
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-python-versions = ">=3.7"
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 python-versions = ">=3.8"
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 [package.dependencies]
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 [package.extras]
 email = ["email-validator (>=2.0.0)"]
+timezone = ["tzdata"]
 
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+    {file = "yarl-1.11.1.tar.gz", hash = "sha256:1bb2d9e212fb7449b8fb73bc461b51eaa17cc8430b4a87d87be7b25052d92f53"},
 ]
 
 [package.dependencies]
@@ -4815,4 +4822,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "d2fdaaea63fa7c9ce606686fd2764ecdeebad38c294ea77d57e641ef26ed959e"
+content-hash = "478f9399dace79ddc93f83eb138fd69c0683cf78fb7ecacac5b9c4fb819ae7b7"
diff --git a/pyfixest/did/did2s.py b/pyfixest/did/did2s.py
index 41711194..a7da5890 100644
--- a/pyfixest/did/did2s.py
+++ b/pyfixest/did/did2s.py
@@ -216,7 +216,7 @@ def _did2s_estimate(
     fit1.fixef()
 
     # demean data
-    Y_hat = fit1.predict(newdata=data)
+    Y_hat = fit1.predict(newdata=data).yhat
     _first_u = data[f"{yname}"].to_numpy().flatten() - Y_hat
     data[f"{yname}_hat"] = _first_u
 
diff --git a/pyfixest/estimation/feols_.py b/pyfixest/estimation/feols_.py
index 8615585f..784632d1 100644
--- a/pyfixest/estimation/feols_.py
+++ b/pyfixest/estimation/feols_.py
@@ -1426,7 +1426,8 @@ def predict(
         atol: float = 1e-6,
         btol: float = 1e-6,
         type: str = "link",
-    ) -> np.ndarray:
+        compute_stdp: bool = False
+    ) -> pd.DataFrame:
         """
         Predict values of the model on new data.
 
@@ -1451,23 +1452,29 @@ def predict(
             Another stopping tolerance for scipy.sparse.linalg.lsqr().
             See https://docs.scipy.org/doc/
                 scipy/reference/generated/scipy.sparse.linalg.lsqr.html
-        link:
+        type:
             The type of prediction to be made. Can be either 'link' or 'response'.
              Defaults to 'link'. 'link' and 'response' lead
             to identical results for linear models.
+        compute_stdp: boolean
+            Whether to compute standard error of the predictions
 
         Returns
         -------
-        y_hat : np.ndarray
-            A flat np.array with predicted values of the regression model.
+        pred_results : pd.DataFrame
+            Dataframe with columns "y_hat" and optionally "stdp" (if compute_stdp is True)
         """
         if self._is_iv:
             raise NotImplementedError(
                 "The predict() method is currently not supported for IV models."
             )
 
+        prediction_df = pd.DataFrame()
         if newdata is None:
-            return self._Y_untransformed.to_numpy().flatten() - self.resid()
+            prediction_df["yhat"] = self._Y_untransformed.to_numpy().flatten() - self._u_hat.flatten()
+            if compute_stdp:
+                prediction_df["stdp"] = self.get_newdata_stdp(self._X)
+            return prediction_df
 
         newdata = _polars_to_pandas(newdata).reset_index(drop=False)
 
@@ -1501,7 +1508,64 @@ def predict(
             _fixef_mat = _apply_fixef_numpy(df_fe.values, fixef_dicts)
             y_hat += np.sum(_fixef_mat, axis=1)
 
-        return y_hat.flatten()
+        prediction_df["yhat"] = y_hat.flatten()
+        if compute_stdp:
+            stdp_df = np.full(newdata.shape[0], np.nan)
+            stdp_df[X_index] = self.get_newdata_stdp(X)
+            prediction_df["stdp"] = stdp_df
+
+        return prediction_df
+
+
+    def get_newdata_stdp(self, X):
+        """
+        Get standard error of predictions for new data.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            Covariates for new data points.
+
+        Returns
+        -------
+        list
+            Standard errors for each prediction
+        """
+        # for now only compute prediction error if model has no fixed effects
+        # TODO: implement for fixed effects
+        if not self._has_fixef:
+            if not self._X_is_empty:
+                return list(map(self.get_single_row_stdp, X))
+            else:
+                warnings.warn(
+                    """
+                    Standard error of the prediction cannot be computed if X is empty.
+                    Prediction dataframe stdp column will be None.
+                    """
+                )
+        else:
+            warnings.warn(
+                """
+                Standard error of the prediction is not implemented for fixed effects models.
+                Prediction dataframe stdp column will be None.
+                """
+            )
+
+    def get_single_row_stdp(self, row):
+        """
+        Get standard error of predictions for a single row.
+
+        Parameters
+        ----------
+        row : np.ndarray
+            Single row of new covariate data
+
+        Returns
+        -------
+        np.ndarray
+            Standard error of prediction for single row
+        """
+        return np.linalg.multi_dot([row, self._vcov, np.transpose(row)])
 
     def get_nobs(self):
         """
diff --git a/pyfixest/estimation/fepois_.py b/pyfixest/estimation/fepois_.py
index 1dac5feb..040dbcc7 100644
--- a/pyfixest/estimation/fepois_.py
+++ b/pyfixest/estimation/fepois_.py
@@ -299,7 +299,8 @@ def predict(
         atol: float = 1e-6,
         btol: float = 1e-6,
         type: str = "link",
-    ) -> np.ndarray:
+        compute_stdp: bool = False
+    ) -> pd.DataFrame:
         """
         Return predicted values from regression model.
 
@@ -334,13 +335,13 @@ def predict(
         btol : Float, default 1e-6
             Another stopping tolerance for scipy.sparse.linalg.lsqr().
             See https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.lsqr.html
-
-
+        compute_stdp: boolean
+            Ignored, included to conform with method signature of feols.predict()
 
         Returns
         -------
-        np.ndarray
-            A flat array with the predicted values of the regression model.
+        pred_results : pd.DataFrame
+            Dataframe with columns "y_hat" with predicted values from regression model
         """
         _Xbeta = self._Xbeta.flatten()
         _has_fixef = self._has_fixef
@@ -355,10 +356,13 @@ def predict(
             )
 
         # y_hat = super().predict(newdata=newdata, type=type, atol=atol, btol=btol)
+        prediction_df = pd.DataFrame()
         if type == "link":
-            return np.exp(_Xbeta)
+            prediction_df["yhat"] = np.exp(_Xbeta)
+            return prediction_df
         elif type == "response":
-            return _Xbeta
+            prediction_df["yhat"] = _Xbeta
+            return prediction_df
         else:
             raise ValueError("type must be one of 'response' or 'link'.")
 
diff --git a/pyproject.toml b/pyproject.toml
index 7d5e4de2..5c6a933c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -37,6 +37,7 @@ wildboottest = ">=0.2.0"
 pre-commit = "^3.6.0"
 doubleml = "^0.7.1"
 marginaleffects = "^0.0.10"
+statsmodels = "^0.14.1"
 
 [tool.poetry.group.docs.dependencies]
 causaldata = "*"
diff --git a/tests/.coverage b/tests/.coverage
index ef76747f..29f0509c 100644
Binary files a/tests/.coverage and b/tests/.coverage differ
diff --git a/tests/test_predict_resid_fixef.py b/tests/test_predict_resid_fixef.py
index f95aa65d..54da5e11 100644
--- a/tests/test_predict_resid_fixef.py
+++ b/tests/test_predict_resid_fixef.py
@@ -37,8 +37,8 @@ def test_internally(data):
     # predict via feols, without fixed effect
     fit = feols(fml="Y~csw(X1, X2)", data=data, vcov="iid")
     mod = fit.fetch_model(0)
-    original_prediction = mod.predict()
-    updated_prediction = mod.predict(newdata=mod._data)
+    original_prediction = mod.predict().yhat
+    updated_prediction = mod.predict(newdata=mod._data).yhat
     np.allclose(original_prediction, updated_prediction)
     assert mod._data.shape[0] == original_prediction.shape[0]
     assert mod._data.shape[0] == updated_prediction.shape[0]
@@ -47,15 +47,15 @@ def test_internally(data):
     fit = feols(fml="Y~csw(X1, X2) | f1", data=data, vcov="iid")
     mod = fit.fetch_model(0)
     mod.fixef()
-    original_prediction = mod.predict()
-    updated_prediction = mod.predict(newdata=mod._data)
+    original_prediction = mod.predict().yhat
+    updated_prediction = mod.predict(newdata=mod._data).yhat
     np.allclose(original_prediction, updated_prediction)
     assert mod._data.shape[0] == original_prediction.shape[0]
     assert mod._data.shape[0] == updated_prediction.shape[0]
 
     # now expect error with updated predicted being a subset of data
     with pytest.raises(ValueError):
-        updated_prediction = mod.predict(newdata=data.iloc[0:100, :])
+        updated_prediction = mod.predict(newdata=data.iloc[0:100, :]).yhat
         np.allclose(original_prediction, updated_prediction)
 
     # fepois NotImplementedError(s)
@@ -123,18 +123,18 @@ def test_vs_fixest(data, fml):
     #    raise ValueError("sumFE for Poisson are not equal")
 
     # test predict for OLS
-    if not np.allclose(feols_mod.predict(), r_fixest_ols.rx2("fitted.values")):
+    if not np.allclose(feols_mod.predict().yhat, r_fixest_ols.rx2("fitted.values")):
         raise ValueError("Predictions for OLS are not equal")
 
-    if not np.allclose(len(feols_mod.predict()), len(stats.predict(r_fixest_ols))):
+    if not np.allclose(len(feols_mod.predict().yhat), len(stats.predict(r_fixest_ols))):
         raise ValueError("Predictions for OLS are not the same length")
     # test predict for Poisson
-    # if not np.allclose(fepois_mod.predict(), r_fixest_pois.rx2("fitted.values")):
+    # if not np.allclose(fepois_mod.predict().yhat, r_fixest_pois.rx2("fitted.values")):
     #    raise ValueError("Predictions for Poisson are not equal")
 
     # test on new data - OLS.
     if not np.allclose(
-        feols_mod.predict(newdata=data2), stats.predict(r_fixest_ols, newdata=data2)
+        feols_mod.predict(newdata=data2).yhat, stats.predict(r_fixest_ols, newdata=data2)
     ):
         raise ValueError("Predictions for OLS are not equal")
 
@@ -171,7 +171,7 @@ def test_predict_nas():
     # test 1
     fml = "Y ~ X1 + X2 | f1"
     fit = feols(fml, data=data)
-    res = fit.predict(newdata=data)
+    res = fit.predict(newdata=data).yhat
     fit_r = fixest.feols(ro.Formula(fml), data=data)
     res_r = stats.predict(fit_r, newdata=data)
     np.testing.assert_allclose(res, res_r, atol=1e-05, rtol=1e-05)
@@ -184,7 +184,7 @@ def test_predict_nas():
 
     fml = "Y ~ X1 + X2 | f1"
     fit = feols(fml, data=data)
-    res = fit.predict(newdata=newdata)
+    res = fit.predict(newdata=newdata).yhat
     fit_r = fixest.feols(ro.Formula(fml), data=data)
     res_r = stats.predict(fit_r, newdata=newdata)
     np.testing.assert_allclose(res, res_r, atol=1e-05, rtol=1e-05)
@@ -192,7 +192,7 @@ def test_predict_nas():
     assert len(res) == len(res_r)
 
     newdata.loc[198, "Y"] = np.nan
-    res = fit.predict(newdata=newdata)
+    res = fit.predict(newdata=newdata).yhat
     res_r = stats.predict(fit_r, newdata=newdata)
     np.testing.assert_allclose(res, res_r, atol=1e-05, rtol=1e-05)
     assert newdata.shape[0] == len(res)
@@ -201,7 +201,7 @@ def test_predict_nas():
     # test 3
     fml = "Y ~ X1 + X2 + f1| f1 "
     fit = feols(fml, data=data)
-    res = fit.predict(newdata=data)
+    res = fit.predict(newdata=data).yhat
     fit_r = fixest.feols(ro.Formula(fml), data=data)
     res_r = stats.predict(fit_r, newdata=data)
     np.testing.assert_allclose(res, res_r, atol=1e-05, rtol=1e-05)
@@ -229,7 +229,7 @@ def test_new_fixef_level(data, fml):
         se="hetero",
     )
 
-    updated_prediction_py = feols_mod.predict(newdata=data2)
+    updated_prediction_py = feols_mod.predict(newdata=data2).yhat
     updated_prediction_r = stats.predict(r_fixest_ols, newdata=data2)
 
     if not np.allclose(updated_prediction_py, updated_prediction_r):
@@ -249,7 +249,7 @@ def test_categorical_covariate_predict():
     df_sub = df.query("x == 1 or x == 2 or x == 3").copy()
 
     py_fit = feols("y ~ C(x, contr.treatment(base=1))", df)
-    py_predict = py_fit.predict(df_sub)
+    py_predict = py_fit.predict(df_sub).yhat
 
     r_predict = np.array(
         [
diff --git a/tests/test_stdp.py b/tests/test_stdp.py
new file mode 100644
index 00000000..8af6b503
--- /dev/null
+++ b/tests/test_stdp.py
@@ -0,0 +1,22 @@
+import numpy as np
+import statsmodels.api as sm
+
+from pyfixest.estimation.estimation import feols
+from pyfixest.utils.utils import get_data
+
+
+def test_stdp():
+    """Compare the standard error of the prediction to statsmodels.get_prediction()."""
+    data = get_data().dropna()
+
+    fit = feols("Y ~ X1 + X2", data=data)
+    stdp = fit.predict(compute_stdp=True).stdp
+
+    X = data[["X1","X2"]]
+    Y = data["Y"]
+    X = sm.add_constant(X)
+    model = sm.OLS(Y, X).fit()
+    predictions = model.get_prediction()
+    stdp_sm = predictions.var_pred_mean
+
+    np.testing.assert_allclose(stdp, stdp_sm)