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CLN: Fix various small lint issues and docs
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13 files changed

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‎doc/source/conf.py‎

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

‎doc/source/types.rst‎

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

‎examples/asset-pricing_formulas.ipynb‎

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

‎examples/iv_advanced-examples.ipynb‎

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

‎examples/iv_basic-examples.ipynb‎

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

‎examples/iv_using-formulas.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from linearmodels.datasets import meps\n",
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"from linearmodels.iv import IV2SLS\n",
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"\n",

‎examples/system_examples.ipynb‎

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

‎examples/system_three-stage-ls.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"from linearmodels import IV2SLS, IV3SLS, SUR, IVSystemGMM"
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"from linearmodels import IV2SLS, IV3SLS, IVSystemGMM"
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]
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},
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{

‎linearmodels/iv/model.py‎

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@@ -963,7 +963,7 @@ class _IVGMMBase(_IVModelBase):
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Observation weights used in estimation
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weight_type : str
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Name of moment condition weight function to use in the GMM estimation
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**weight_config
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weight_config
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Additional keyword arguments to pass to the moment condition weight
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function
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Observation weights used in estimation
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weight_type : str
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Name of moment condition weight function to use in the GMM estimation
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**weight_config
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weight_config
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Additional keyword arguments to pass to the moment condition weight
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function
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Observation weights used in estimation
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weight_type : str
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Name of moment condition weight function to use in the GMM estimation
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**weight_config
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weight_config
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Additional keyword arguments to pass to the moment condition weight
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function
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Observation weights used in estimation
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weight_type : str
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Name of moment condition weight function to use in the GMM estimation
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**weight_config
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weight_config
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Additional keyword arguments to pass to the moment condition weight
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function
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Observation weights used in estimation
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weight_type : str
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Name of moment condition weight function to use in the GMM estimation
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**weight_config
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weight_config
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Additional keyword arguments to pass to the moment condition weight
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function
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Observation weights used in estimation
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weight_type : str
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Name of moment condition weight function to use in the GMM estimation
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**weight_config
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weight_config
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Additional keyword arguments to pass to the moment condition weight
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function
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‎linearmodels/panel/data.py‎

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copy : bool
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Flag indicating whether to copy the input. Only has an effect when
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x is a DataFrame
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cast : bool
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Flag indicating to case the data to double precision.
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Notes
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-----

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