CLN: Fix various small lint issues and docs

doc/source/conf.py

@@ -114,7 +114,7 @@
 # You can specify multiple suffix as a list of string:
 #
 # source_suffix = [".rst", ".md"]
-source_suffix = ".rst"
+source_suffix = {".rst": "restructuredtext"}
 
 # The master toctree document.
 master_doc = "index"

doc/source/types.rst

@@ -1,27 +1,13 @@
 Type Aliases
 ============
 
-.. module:: linearmodels.typing.data
-
-.. automodule:: linearmodels.typing.data
-
-.. autoclass::
-
-   ArrayLike
-   Float64Array
-   Int64Array
-   Int32Array
-   IntArray
-   BoolArray
-   AnyArray
-   Uint32Array = np.ndarray[Any, np.dtype[np.uint32]]  # pragma: no cover
-
-
-.. module:: linearmodels.typing
-
-.. automodule:: linearmodels.typing
-
-.. autoclass::
-
-   Numeric
-   OptionalNumeric
+.. autoclass:: linearmodels.typing.Numeric
+.. autoclass:: linearmodels.typing.OptionalNumeric
+.. autoclass:: linearmodels.typing.data.ArrayLike
+.. autoclass:: linearmodels.typing.data.Float64Array
+.. autoclass:: linearmodels.typing.data.Int64Array
+.. autoclass:: linearmodels.typing.data.Int32Array
+.. autoclass:: linearmodels.typing.data.IntArray
+.. autoclass:: linearmodels.typing.data.BoolArray
+.. autoclass:: linearmodels.typing.data.AnyArray
+.. autoclass:: linearmodels.typing.data.Uint32Array

examples/asset-pricing_formulas.ipynb

@@ -67,7 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from linearmodels.asset_pricing import LinearFactorModel, TradedFactorModel\n",
+    "from linearmodels.asset_pricing import LinearFactorModel\n",
     "\n",
     "formula = \"NoDur + Chems + S1V1 + S5V5 + S1M1 + S5M5 ~ MktRF + HML + Mom\"\n",
     "mod = LinearFactorModel.from_formula(formula, data)\n",

examples/iv_advanced-examples.ipynb

@@ -115,7 +115,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from statsmodels.api import OLS, add_constant\n",
     "\n",
     "data[\"const\"] = 1\n",
     "controls = [\"const\"] + controls"

examples/iv_basic-examples.ipynb

@@ -573,9 +573,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "v = IV2SLS(data[endog], data[exog + instr], None, None).fit().resids\n",
     "import pandas as pd\n",
     "\n",
+    "v = IV2SLS(data[endog], data[exog + instr], None, None).fit().resids\n",
+    "\n",
     "res_direct = IV2SLS(\n",
     "    data[dep], pd.concat([v, data[exog]], axis=1), data[endog], data[instr]\n",
     ").fit(cov_type=\"unadjusted\")\n",
@@ -802,7 +803,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/iv_using-formulas.ipynb

@@ -72,7 +72,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "from linearmodels.datasets import meps\n",
     "from linearmodels.iv import IV2SLS\n",
     "\n",

examples/system_examples.ipynb

@@ -161,7 +161,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
     "from linearmodels.system import SUR\n",
     "\n",
     "mod = SUR(mod_data)\n",
@@ -181,14 +180,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
     "cov = res.sigma\n",
     "std = np.sqrt(np.diag(res.sigma)[:, None])\n",
     "regions = [k for k in mod_data.keys()]\n",
     "corr = pd.DataFrame(cov / (std @ std.T), columns=regions, index=regions)\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
     "sns.heatmap(corr, vmax=0.8, square=True)\n",
     "plt.show()\n",
     "\n",
@@ -780,7 +779,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.7"
   },
   "pycharm": {
    "stem_cell": {

examples/system_three-stage-ls.ipynb

@@ -75,7 +75,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from linearmodels import IV2SLS, IV3SLS, SUR, IVSystemGMM"
+    "from linearmodels import IV2SLS, IV3SLS, IVSystemGMM"
    ]
   },
   {

linearmodels/iv/model.py

@@ -963,7 +963,7 @@ class _IVGMMBase(_IVModelBase):
         Observation weights used in estimation
     weight_type : str
         Name of moment condition weight function to use in the GMM estimation
-    **weight_config
+    weight_config
         Additional keyword arguments to pass to the moment condition weight
         function
 
@@ -1066,7 +1066,7 @@ class IVGMM(_IVGMMBase):
         Observation weights used in estimation
     weight_type : str
         Name of moment condition weight function to use in the GMM estimation
-    **weight_config
+    weight_config
         Additional keyword arguments to pass to the moment condition weight
         function
 
@@ -1139,7 +1139,7 @@ def from_formula(
             Observation weights used in estimation
         weight_type : str
             Name of moment condition weight function to use in the GMM estimation
-        **weight_config
+        weight_config
             Additional keyword arguments to pass to the moment condition weight
             function
 
@@ -1342,7 +1342,7 @@ class IVGMMCUE(_IVGMMBase):
         Observation weights used in estimation
     weight_type : str
         Name of moment condition weight function to use in the GMM estimation
-    **weight_config
+    weight_config
         Additional keyword arguments to pass to the moment condition weight
         function
 
@@ -1418,7 +1418,7 @@ def from_formula(
             Observation weights used in estimation
         weight_type : str
             Name of moment condition weight function to use in the GMM estimation
-        **weight_config
+        weight_config
             Additional keyword arguments to pass to the moment condition weight
             function
 
@@ -1706,7 +1706,7 @@ def _gmm_model_from_formula(
         Observation weights used in estimation
     weight_type : str
         Name of moment condition weight function to use in the GMM estimation
-    **weight_config
+    weight_config
         Additional keyword arguments to pass to the moment condition weight
         function
 

linearmodels/panel/data.py

@@ -140,8 +140,6 @@ class PanelData:
     copy : bool
         Flag indicating whether to copy the input. Only has an effect when
         x is a DataFrame
-    cast : bool
-        Flag indicating to case the data to double precision.
 
     Notes
     -----

linearmodels/system/model.py

@@ -1843,7 +1843,7 @@ class IVSystemGMM(_SystemModelBase):
         if weight_type is "unadjusted"
     weight_type : str
         Name of moment condition weight function to use in the GMM estimation
-    **weight_config
+    weight_config
         Additional keyword arguments to pass to the moment condition weight
         function
 
@@ -2185,7 +2185,7 @@ def from_formula(
             * "unadjusted", "homoskedastic" - Assume moments are homoskedastic
             * "robust", "heteroskedastic" - Allow for heteroskedasticity
 
-        **weight_config
+        weight_config
             Additional keyword arguments to pass to the moment condition weight
             function
 

linearmodels/typing/__init__.py

@@ -1,7 +1,5 @@
-from __future__ import annotations
-
 from collections.abc import Hashable, Sequence
-from typing import TYPE_CHECKING, Any, Optional, Union
+from typing import Any, Optional, Union
 
 import numpy as np
 from pandas import DataFrame, Series
@@ -32,19 +30,14 @@
 AnyPandas = Union[Series, DataFrame]
 Label = Optional[Hashable]
 
-if TYPE_CHECKING:
-    Float64Array = np.ndarray[Any, np.dtype[np.float64]]  # pragma: no cover
-    Int64Array = np.ndarray[Any, np.dtype[np.int64]]  # pragma: no cover
-    Int32Array = np.ndarray[Any, np.dtype[np.int32]]  # pragma: no cover
-    IntArray = np.ndarray[Any, np.dtype[np.int_]]  # pragma: no cover
-    BoolArray = np.ndarray[Any, np.dtype[np.bool_]]  # pragma: no cover
-    AnyArray = np.ndarray[Any, Any]  # pragma: no cover
-    NumericArray = Union[  # pragma: no cover
-        np.ndarray[Any, np.dtype[np.signedinteger[Any]]],  # pragma: no cover
-        np.ndarray[Any, np.dtype[np.unsignedinteger[Any]]],  # pragma: no cover
-        np.ndarray[Any, np.dtype[np.floating[Any]]],  # pragma: no cover
-    ]  # pragma: no cover
-else:
-    IntArray = Float64Array = Int64Array = Int32Array = BoolArray = AnyArray = (
-        NumericArray
-    ) = np.ndarray
+Float64Array = np.ndarray[Any, np.dtype[np.float64]]  # pragma: no cover
+Int64Array = np.ndarray[Any, np.dtype[np.int64]]  # pragma: no cover
+Int32Array = np.ndarray[Any, np.dtype[np.int32]]  # pragma: no cover
+IntArray = np.ndarray[Any, np.dtype[np.int_]]  # pragma: no cover
+BoolArray = np.ndarray[Any, np.dtype[np.bool_]]  # pragma: no cover
+AnyArray = np.ndarray[Any, Any]  # pragma: no cover
+NumericArray = Union[  # pragma: no cover
+    np.ndarray[Any, np.dtype[np.signedinteger[Any]]],  # pragma: no cover
+    np.ndarray[Any, np.dtype[np.unsignedinteger[Any]]],  # pragma: no cover
+    np.ndarray[Any, np.dtype[np.floating[Any]]],  # pragma: no cover
+]  # pragma: no cover

linearmodels/typing/data.py

@@ -1,6 +1,4 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any, Union
+from typing import Any, Union
 
 import numpy as np
 import pandas as pd
@@ -18,18 +16,13 @@
 
 NDArray = Union[np.ndarray]
 
-if TYPE_CHECKING:
-    Float64Array = np.ndarray[Any, np.dtype[np.float64]]  # pragma: no cover
-    Int64Array = np.ndarray[Any, np.dtype[np.int64]]  # pragma: no cover
-    Int32Array = np.ndarray[Any, np.dtype[np.int32]]  # pragma: no cover
-    IntArray = np.ndarray[Any, np.dtype[np.int_]]  # pragma: no cover
-    BoolArray = np.ndarray[Any, np.dtype[np.bool_]]  # pragma: no cover
-    AnyArray = np.ndarray[Any, Any]  # pragma: no cover
-    Uint32Array = np.ndarray[Any, np.dtype[np.uint32]]  # pragma: no cover
-else:
-    Uint32Array = IntArray = Float64Array = Int64Array = Int32Array = BoolArray = (
-        AnyArray
-    ) = NDArray
+Float64Array = np.ndarray[Any, np.dtype[np.float64]]  # pragma: no cover
+Int64Array = np.ndarray[Any, np.dtype[np.int64]]  # pragma: no cover
+Int32Array = np.ndarray[Any, np.dtype[np.int32]]  # pragma: no cover
+IntArray = np.ndarray[Any, np.dtype[np.int_]]  # pragma: no cover
+BoolArray = np.ndarray[Any, np.dtype[np.bool_]]  # pragma: no cover
+AnyArray = np.ndarray[Any, Any]  # pragma: no cover
+Uint32Array = np.ndarray[Any, np.dtype[np.uint32]]  # pragma: no cover
 
 __all__ = [
     "Float64Array",