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DOC: Restructure the docs
Restructure to be simpler to read
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‎.gitignore

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@@ -15,3 +15,5 @@ __pycache__/
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**/.hypothesis/
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dist/
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doc/build/
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doc/source/**/generated/
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arch/univariate/recursions.c

‎arch/bootstrap/multiple_comparison.py

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@@ -90,6 +90,7 @@ class MCS(MultipleComparison):
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Methods
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-------
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compute
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Compute the set ofmodels in the confidence set.
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References
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----------

‎arch/univariate/distribution.py

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References
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----------
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.. [1] Hansen, B. E. (1994). Autoregressive conditional density estimation.
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*International Economic Review*, 35(3), 705–730.
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<http://www.ssc.wisc.edu/~bhansen/papers/ier_94.pdf>
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*International Economic Review*, 35(3), 705–730.
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<http://www.ssc.wisc.edu/~bhansen/papers/ier_94.pdf>
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"""
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‎doc/requirements.txt

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numpy
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scipy
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ipython[notebook]>=3
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ipython>=6
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Cython
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matplotlib
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pandas
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patsy
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statsmodels
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sphinx>=1.5
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sphinx>=1.8
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seaborn
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numpydoc
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nbsphinx
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sphinx_material
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jupyter
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property_cached
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notebook

‎doc/source/_templates/autosummary/attribute.rst

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:orphan:
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{{ fullname | escape | underline}}
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.. currentmodule:: {{ module }}
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.. auto{{ objtype }}:: {{ objname }}
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‎doc/source/_templates/autosummary/class.rst

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{{ fullname | escape | underline}}
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.. currentmodule:: {{ module }}
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.. autoclass:: {{ objname }}
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:exclude-members:
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{% block methods %}
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{% if methods %}
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.. rubric:: Methods
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.. autosummary::
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:toctree: generated/
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{% for item in methods %}
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{%- if not item.startswith('_') or item in ['__call__'] %} ~{{ name }}.{{ item }}
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{% endif %}
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{%- endfor %}
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{% endif %}
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{% endblock %}
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{% block attributes %}
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{% if attributes %}
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.. rubric:: Properties
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.. autosummary::
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:toctree: generated/
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{% for item in attributes %}
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{%- if not item.startswith('_') or item in ['__call__'] %} ~{{ name }}.{{ item }}
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{% endif %}
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{%- endfor %}
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{% endif %}
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{% endblock %}

‎doc/source/_templates/autosummary/member.rst

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:orphan:
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{{ fullname | escape | underline}}
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.. currentmodule:: {{ module }}
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.. auto{{ objtype }}:: {{ objname }}
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‎doc/source/_templates/autosummary/method.rst

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:orphan:
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{{ fullname | escape | underline}}
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.. currentmodule:: {{ module }}
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.. auto{{ objtype }}:: {{ objname }}
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‎doc/source/_templates/autosummary/minimal_module.rst

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{{ fullname | escape | underline}}
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.. automodule:: {{ fullname }}
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{% block docstring %}
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{% endblock %}
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‎doc/source/bootstrap/bootstrap.rst

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.. module:: arch.bootstrap
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:synopsis: Bootstrap methods for simulation and parameter inference
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.. py:module::arch.bootstrap
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.. currentmodule::arch.bootstrap
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The bootstrap module provides both high- and low-level interfaces for
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bootstrapping data contained in NumPy arrays or pandas Series or DataFrames.

‎doc/source/bootstrap/iid-bootstraps.rst

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.. py:currentmodule:: arch.bootstrap
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.. module:: arch.bootstrap
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:noindex:
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.. currentmodule:: arch.bootstrap
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Independent, Identical Distributed Data (i.i.d.)
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------------------------------------------------
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:py:class:`~arch.bootstrap.IIDBootstrap` is the standard bootstrap that is appropriate for data that is
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:class:`~arch.bootstrap.IIDBootstrap` is the standard bootstrap that is appropriate for data that is
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either i.i.d. or at least not serially dependant.
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.. autoclass:: IIDBootstrap
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:members: conf_int, var, cov, apply, bootstrap, reset, seed, set_state, get_state
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.. autosummary::
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:toctree: generated/
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IIDBootstrap
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Independent Samples
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-------------------
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:py:class:`~arch.bootstrap.IndependentSamplesBootstrap` is a bootstrap that is appropriate for
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:class:`~arch.bootstrap.IndependentSamplesBootstrap` is a bootstrap that is appropriate for
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data is totally independent, and where each variable may have a different sample size. This
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type of data arises naturally in experimental settings, e.g., website A/B testing.
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.. autoclass:: IndependentSamplesBootstrap
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:members: index, conf_int, var, cov, apply, bootstrap, reset, seed, set_state, get_state
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.. autosummary::
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:toctree: generated/
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~arch.bootstrap.IndependentSamplesBootstrap

‎doc/source/bootstrap/timeseries-bootstraps.rst

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.. py:currentmodule:: arch.bootstrap
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.. module:: arch.bootstrap
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:noindex:
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.. currentmodule:: arch.bootstrap
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Time-series Bootstraps
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----------------------
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Bootstraps for time-series data come in a variety of forms. The three contained
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in this package are the stationary bootstrap
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(:py:class:`~arch.bootstrap.StationaryBootstrap`), which uses blocks with an
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(:class:`~arch.bootstrap.StationaryBootstrap`), which uses blocks with an
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exponentially distributed lengths, the circular block bootstrap
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(:py:class:`~arch.bootstrap.CircularBlockBootstrap`), which uses
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(:class:`~arch.bootstrap.CircularBlockBootstrap`), which uses
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fixed length blocks, and the moving block bootstrap which also uses fixed
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length blocks (:py:class:`~arch.bootstrap.MovingBlockBootstrap`). The moving
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length blocks (:class:`~arch.bootstrap.MovingBlockBootstrap`). The moving
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block bootstrap does *not* wrap around and so observations near the start or
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end of the series will be systematically under-sampled. It is not recommended
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for this reason.
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The Stationary Bootstrap
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========================
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.. autosummary::
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:toctree: generated/
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.. autoclass:: StationaryBootstrap
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:members: conf_int, var, cov, apply, bootstrap, reset, seed, set_state, get_state
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The Circular Block Bootstrap
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============================
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.. autoclass:: CircularBlockBootstrap
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:members: conf_int, var, cov, apply, bootstrap, reset, seed, set_state, get_state
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The Moving Block Bootstrap
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==========================
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.. autoclass:: MovingBlockBootstrap
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:members: conf_int, var, cov, apply, bootstrap, reset, seed, set_state, get_state
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StationaryBootstrap
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CircularBlockBootstrap
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MovingBlockBootstrap

‎doc/source/conf.py

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# html_sidebars = {}
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if not on_rtd:
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html_sidebars = {
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"**": ["logo-text.html", "globaltoc.html", "searchbox.html"]
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"**": ["logo-text.html", "globaltoc.html", "searchbox.html",
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"localtoc.html"]
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}
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}
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extlinks = {"issue": ("https://github.com/bashtage/arch/issues/%s", "GH")}
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napoleon_use_admonition_for_examples = True
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napoleon_use_admonition_for_notes = False
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napoleon_use_admonition_for_references = True
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autosummary_generate = True
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autoclass_content = 'class'

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

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.. module:: arch.bootstrap.multiple_comparison
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:synopsis: Multiple comparison procedures
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.. py:currentmodule:: arch.bootstrap
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.. currentmodule:: arch.bootstrap
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Test of Superior Predictive Ability (SPA), Reality Check
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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 :py:class:`~arch.bootstrap.RealityCheck`
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**Note**: Also callable using :class:`~arch.bootstrap.RealityCheck`
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.. autosummary::
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:toctree: generated/
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~arch.bootstrap.SPA
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.. autoclass:: SPA
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:members: compute, pvalues, critical_values
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Stepwise Multiple Testing (StepM)
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=================================
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The Stepwise Multiple Testing procedure (Romano & Wolf (2005)) is closely
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related to the SPA, except that it returns a set of models that are superior
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to the benchmark model, rather than the p-value from the null. They are
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so closely related that :py:class:`~arch.bootstrap.StepM` is essentially
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a wrapper around :py:class:`~arch.bootstrap.SPA` with some small modifications
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so closely related that :class:`~arch.bootstrap.StepM` is essentially
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a wrapper around :class:`~arch.bootstrap.SPA` with some small modifications
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to allow multiple calls.
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.. autoclass:: StepM
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:members: compute, superior_models
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.. autosummary::
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:toctree: generated/
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~arch.bootstrap.StepM
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Model Confidence Set (MCS)
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==========================
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in the model confidence set. Like the other MCPs, it controls the
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.. autoclass:: MCS
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:members: compute
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.. autosummary::
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:toctree: generated/
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~arch.bootstrap.MCS

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

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a the `Familywise Error Rate <https://www.google.com/search?q=familywise+error+rate>`_,
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which is similar to the size of a test.
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‎doc/source/unitroot/tests.rst

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.. module:: arch.unitroot
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:synopsis: Unit root tests
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.. py:currentmodule:: arch.unitroot
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.. currentmodule:: arch.unitroot
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.. autosummary::
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:toctree: generated/
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Augmented-Dickey Fuller Testing
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-------------------------------
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.. autoclass:: ADF
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:members: stat, pvalue, critical_values, summary, regression, trend, lags, null_hypothesis, alternative_hypothesis, valid_trends
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Dickey-Fuller GLS Testing
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.. autoclass:: DFGLS
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:members: summary
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Phillips-Perron Testing
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-----------------------
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.. autoclass:: PhillipsPerron
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Zivot-Andrews Testing
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.. autoclass:: ZivotAndrews
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:members: summary
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Variance Ratios
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.. autoclass:: VarianceRatio
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:members: summary
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KPSS Testing
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.. autoclass:: KPSS
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ADF
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DFGLS
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PhillipsPerron
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ZivotAndrews
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VarianceRatio
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KPSS
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.. autofunction:: auto_bandwidth
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:toctree: generated/
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auto_bandwidth

‎doc/source/unitroot/unitroot.rst

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Unit Root Testing
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Many time series are highly persistent, and determining whether the data appear
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to be stationary or contains a unit root is the first step in many analyses.
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This module contains a number of routines:
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* Augmented Dickey-Fuller (:py:class:`~arch.unitroot.ADF`)
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* Dickey-Fuller GLS (:py:class:`~arch.unitroot.DFGLS`)
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* Phillips-Perron (:py:class:`~arch.unitroot.PhillipsPerron`)
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* KPSS (:py:class:`~arch.unitroot.KPSS`)
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* Zivot-Andrews (:py:class:`~arch.unitroot.ZivotAndrews`)
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* Variance Ratio (:py:class:`~arch.unitroot.VarianceRatio`)
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* Automatic Bandwidth Selection (:py:func:`arch.unitroot.auto_bandwidth`)
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* Augmented Dickey-Fuller (:class:`~arch.unitroot.ADF`)
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* Dickey-Fuller GLS (:class:`~arch.unitroot.DFGLS`)
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* Phillips-Perron (:class:`~arch.unitroot.PhillipsPerron`)
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* KPSS (:class:`~arch.unitroot.KPSS`)
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* Zivot-Andrews (:class:`~arch.unitroot.ZivotAndrews`)
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* Variance Ratio (:class:`~arch.unitroot.VarianceRatio`)
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* Automatic Bandwidth Selection (:func:`arch.unitroot.auto_bandwidth`)
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The first four all start with the null of a unit root and have an alternative
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of a stationary process. The final test, KPSS, has a null of a stationary

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