TYP: Typing improvements

Clarify types in some cases

Author: bashtage

linearmodels/iv/results.py

@@ -286,7 +286,7 @@ def conf_int(self, level: float = 0.95) -> DataFrame:
         -----
         Uses a t(df_resid) if ``debiased`` is True, else normal.
         """
-        ci_quantiles = [(1 - level) / 2, 1 - (1 - level) / 2]
+        ci_quantiles = array([(1 - level) / 2, 1 - (1 - level) / 2])
         if self._debiased:
             q = stats.t.ppf(ci_quantiles, self.df_resid)
         else:
@@ -1348,6 +1348,7 @@ def anderson_rubin(self) -> InvalidTestStatistic | WaldTestStatistic:
                 "Test requires more instruments than " "endogenous variables.",
                 name=name,
             )
+        assert self._liml_kappa is not None
         stat = nobs * log(self._liml_kappa)
         df = ninstr - nendog
         null = "The model is not overidentified."
@@ -1385,6 +1386,7 @@ def basmann_f(self) -> InvalidTestStatistic | WaldTestStatistic:
             )
         df = ninstr - nendog
         df_denom = nobs - (nexog + ninstr)
+        assert self._liml_kappa is not None
         stat = (self._liml_kappa - 1) * df_denom / df
         null = "The model is not overidentified."
         return WaldTestStatistic(stat, null, df, df_denom=df_denom, name=name)

linearmodels/panel/model.py

@@ -1521,6 +1521,8 @@ def _lsmr_path(
             cond_mean = lsmr(wd, wx[:, i], atol=1e-8, btol=1e-8)[0]
             cond_mean /= cond
             wx_mean_l.append(cond_mean)
+        wx_mean: linearmodels.typing.data.Float64Array | csc_matrix
+        wy_mean: linearmodels.typing.data.Float64Array | csc_matrix
         wx_mean = np.column_stack(wx_mean_l)
         wy_mean = lsmr(wd, wy, atol=1e-8, btol=1e-8)[0]
         wy_mean /= cond

linearmodels/panel/results.py

@@ -333,7 +333,7 @@ def conf_int(self, level: float = 0.95) -> DataFrame:
         -----
         Uses a t(df_resid) if ``debiased`` is True, else normal.
         """
-        ci_quantiles = [(1 - level) / 2, 1 - (1 - level) / 2]
+        ci_quantiles = np.array([(1 - level) / 2, 1 - (1 - level) / 2])
         if self._debiased:
             q = stats.t.ppf(ci_quantiles, self.df_resid)
         else: