DOC: Small fixes for doc building

doc/source/changes/4.0.rst

@@ -39,7 +39,7 @@ Release 4.16
 Release 4.15
 ============
 - This is a minor release with doc fixes and other small updates. The only notable
-  feature is :func:`~arch.unitroot.unitroot.PhillipsPerron.regression` which returns
+  feature is :meth:`~arch.unitroot.unitroot.PhillipsPerron.regression` which returns
   regression results from the model estimated as part of the test (:issue:`395`).
 
 Release 4.14
@@ -161,7 +161,7 @@ Release 4.7
 Release 4.6
 ===========
 - Added support for MIDAS volatility processes using Hyperbolic
-  weighting in :class:`~arch.univariate.MidasHyperbolic`
+  weighting in :class:`~arch.univariate.MIDASHyperbolic`
   (:issue:`233`).
 
 Release 4.5

doc/source/changes/5.0.rst

@@ -22,7 +22,7 @@ Release 5.4
 
 Release 5.3
 ===========
-- Fixed a bug in :func:`~arch.univariate.mean.arch_model` where ``power`` was
+- Fixed a bug in :func:`~arch.univariate.arch_model` where ``power`` was
   not passed to the :class:`~arch.univariate.FIGARCH` constructor (:issue:`572`).
 - Fixed a bug that affected downstream projects due to an overly specific assert
   (:issue:`569`).
@@ -75,8 +75,8 @@ Volatility Modeling
   instances and integers.  If an integer is passed, the random number generator is constructed by
   calling :func:`numpy.random.default_rng` The ``seed`` keyword argument replaces the
   ``random_state`` keyword argument.
-- The :func:`~arch.univariate.distribution.Normal.random_state` property has also been deprecated in favor
-  of :func:`~arch.univariate.distribution.Normal.generator`.
+- The :meth:`~arch.univariate.distribution.Normal.random_state` property has also been deprecated in favor
+  of :meth:`~arch.univariate.distribution.Normal.generator`.
 - Added :class:`~arch.univariate.ARCHInMean` mean process supporting (G)ARCH-in-mean models.
 - Extended :class:`~arch.univariate.volatility.VolatilityProcess` with
   :func:`~arch.univariate.volatility.VolatilityProcess.volatility_updater` that contains a

doc/source/conf.py

@@ -270,14 +270,12 @@
 
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
-    "statsmodels": ("https://www.statsmodels.org/dev/", None),
-    "matplotlib": ("https://matplotlib.org/stable/", None),
-    "scipy": ("https://docs.scipy.org/doc/scipy/", None),
-    "python": ("https://docs.python.org/3/", None),
-    "numpy": ("https://numpy.org/doc/stable/", None),
-    "np": ("https://numpy.org/doc/stable/", None),
-    "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
-    "pd": ("https://pandas.pydata.org/pandas-docs/stable/", None),
+    "statsmodels": ("https://www.statsmodels.org/dev", None),
+    "matplotlib": ("https://matplotlib.org/stable", None),
+    "scipy": ("https://docs.scipy.org/doc/scipy", None),
+    "python": ("https://docs.python.org/3", None),
+    "numpy": ("https://numpy.org/doc/stable", None),
+    "pandas": ("https://pandas.pydata.org/pandas-docs/stable", None),
 }
 
 extlinks = {"issue": ("https://github.com/bashtage/arch/issues/%s", "GH%s")}

doc/source/multiple-comparison/multiple-comparison-reference.rst

@@ -28,7 +28,7 @@ from the benchmark model.  The alternative is
 This procedure accounts for dependence between the losses and the fact that
 there are potentially alternative models being considered.
 
-**Note**: Also callable using :class:`~arch.bootstrap.RealityCheck`
+**Note**: Also callable using ``arch.bootstrap.RealityCheck``
 
 
 .. autosummary::

doc/source/univariate/results.rst

@@ -1,8 +1,8 @@
 Model Results
 =============
-All model return the same object, a results class (:class:`ARCHModelResult`).
-When using the ``fix`` method, a (:class:`ARCHModelFixedResult`) is produced
-that lacks some properties of a (:class:`ARCHModelResult`) that are not
+All model return the same object, a results class (:class:`~arch.univariate.base.ARCHModelResult`).
+When using the ``fix`` method, a (:class:`~arch.univariate.base.ARCHModelFixedResult`) is produced
+that lacks some properties of a (:class:`~arch.univariate.base.ARCHModelResult`) that are not
 relevant when parameters are not estimated.