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Merge pull request #744 from bashtage/doc-fixes
DOC: Small fixes for doc building
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doc/source/changes/4.0.rst

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

doc/source/changes/5.0.rst

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

doc/source/conf.py

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

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

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This procedure accounts for dependence between the losses and the fact that
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there are potentially alternative models being considered.
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**Note**: Also callable using :class:`~arch.bootstrap.RealityCheck`
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**Note**: Also callable using ``arch.bootstrap.RealityCheck``
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.. autosummary::

doc/source/univariate/results.rst

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