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DOC: Finalize changes for 4.12 release
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bashtage authored Feb 3, 2020
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160 changes: 80 additions & 80 deletions README.md
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Expand Up @@ -6,31 +6,31 @@ Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for
financial econometrics, written in Python (with Cython and/or Numba used
to improve performance)

| | |
| :-------- | :------------- |
| **Latest Release** | [![PyPI version](https://badge.fury.io/py/arch.svg)](https://badge.fury.io/py/arch) |
| | [![Anaconda-Server Badge](https://anaconda.org/bashtage/arch/badges/version.svg)](https://anaconda.org/bashtage/arch) |
| **Continuous Integration** | [![Travis Build Status](https://travis-ci.org/bashtage/arch.svg?branch=master)](https://travis-ci.org/bashtage/arch) |
| | [![Appveyor Build Status](https://ci.appveyor.com/api/projects/status/nmt02u7jwcgx7i2x?svg=true)](https://ci.appveyor.com/project/bashtage/arch/branch/master) |
| **Coverage** | [![Coverage Status](https://coveralls.io/repos/github/bashtage/arch/badge.svg?branch=master)](https://coveralls.io/r/bashtage/arch?branch=master) |
| | [![codecov](https://codecov.io/gh/bashtage/arch/branch/master/graph/badge.svg)](https://codecov.io/gh/bashtage/arch) |
| **Code Quality** | [![Code Quality: Python](https://img.shields.io/lgtm/grade/python/g/bashtage/arch.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/bashtage/arch/context:python) |
| | [![Total Alerts](https://img.shields.io/lgtm/alerts/g/bashtage/arch.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/bashtage/arch/alerts) |
| | [![Codacy Badge](https://api.codacy.com/project/badge/Grade/93f6fd90209842bf97fd20fda8db70ef)](https://www.codacy.com/manual/bashtage/arch?utm_source=github.com&utm_medium=referral&utm_content=bashtage/arch&utm_campaign=Badge_Grade) |
| | [![codebeat badge](https://codebeat.co/badges/18a78c15-d74b-4820-b56d-72f7e4087532)](https://codebeat.co/projects/github-com-bashtage-arch-master) |
| **Citation** | [![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.3551028.svg)](https://doi.org/10.5281/zenodo.3551028) |
| **Documentation** | [![Documentation Status](https://readthedocs.org/projects/arch/badge/?version=latest)](http://arch.readthedocs.org/en/latest/) |
| Metric | |
| :------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Latest Release** | [![PyPI version](https://badge.fury.io/py/arch.svg)](https://badge.fury.io/py/arch) |
| | [![Anaconda-Server Badge](https://anaconda.org/bashtage/arch/badges/version.svg)](https://anaconda.org/bashtage/arch) |
| **Continuous Integration** | [![Travis Build Status](https://travis-ci.org/bashtage/arch.svg?branch=master)](https://travis-ci.org/bashtage/arch) |
| | [![Appveyor Build Status](https://ci.appveyor.com/api/projects/status/nmt02u7jwcgx7i2x?svg=true)](https://ci.appveyor.com/project/bashtage/arch/branch/master) |
| **Coverage** | [![Coverage Status](https://coveralls.io/repos/github/bashtage/arch/badge.svg?branch=master)](https://coveralls.io/r/bashtage/arch?branch=master) |
| | [![codecov](https://codecov.io/gh/bashtage/arch/branch/master/graph/badge.svg)](https://codecov.io/gh/bashtage/arch) |
| **Code Quality** | [![Code Quality: Python](https://img.shields.io/lgtm/grade/python/g/bashtage/arch.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/bashtage/arch/context:python) |
| | [![Total Alerts](https://img.shields.io/lgtm/alerts/g/bashtage/arch.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/bashtage/arch/alerts) |
| | [![Codacy Badge](https://api.codacy.com/project/badge/Grade/93f6fd90209842bf97fd20fda8db70ef)](https://www.codacy.com/manual/bashtage/arch?utm_source=github.com&utm_medium=referral&utm_content=bashtage/arch&utm_campaign=Badge_Grade) |
| | [![codebeat badge](https://codebeat.co/badges/18a78c15-d74b-4820-b56d-72f7e4087532)](https://codebeat.co/projects/github-com-bashtage-arch-master) |
| **Citation** | [![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.3551028.svg)](https://doi.org/10.5281/zenodo.3551028) |
| **Documentation** | [![Documentation Status](https://readthedocs.org/projects/arch/badge/?version=latest)](http://arch.readthedocs.org/en/latest/) |

## Module Contents

* [Univariate ARCH Models](#volatility)
* [Unit Root Tests](#unit-root)
* [Bootstrapping](#bootstrap)
* [Multiple Comparison Tests](#multiple-comparison)
- [Univariate ARCH Models](#volatility)
- [Unit Root Tests](#unit-root)
- [Bootstrapping](#bootstrap)
- [Multiple Comparison Tests](#multiple-comparison)

### Python 3

``arch`` is Python 3 only. Version 4.8 is the final version that supported Python 2.7.
`arch` is Python 3 only. Version 4.8 is the final version that supported Python 2.7.

## Documentation

Expand All @@ -46,34 +46,34 @@ research available at [Kevin Sheppard's site](http://www.kevinsheppard.com).

## Contributing

Contributions are welcome. There are opportunities at many levels to contribute:
Contributions are welcome. There are opportunities at many levels to contribute:

* Implement new volatility process, e.g., FIGARCH
* Improve docstrings where unclear or with typos
* Provide examples, preferably in the form of IPython notebooks
- Implement new volatility process, e.g., FIGARCH
- Improve docstrings where unclear or with typos
- Provide examples, preferably in the form of IPython notebooks

## Examples

<a id="volatility"></a>

### Volatility Modeling

* Mean models
* Constant mean
* Heterogeneous Autoregression (HAR)
* Autoregression (AR)
* Zero mean
* Models with and without exogenous regressors
* Volatility models
* ARCH
* GARCH
* TARCH
* EGARCH
* EWMA/RiskMetrics
* Distributions
* Normal
* Student's T
* Generalized Error Distribution
- Mean models
- Constant mean
- Heterogeneous Autoregression (HAR)
- Autoregression (AR)
- Zero mean
- Models with and without exogenous regressors
- Volatility models
- ARCH
- GARCH
- TARCH
- EGARCH
- EWMA/RiskMetrics
- Distributions
- Normal
- Student's T
- Generalized Error Distribution

See the [univariate volatility example notebook](http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/univariate_volatility_modeling.ipynb) for a more complete overview.

Expand All @@ -94,31 +94,31 @@ res = am.fit()

### Unit Root Tests

* Augmented Dickey-Fuller
* Dickey-Fuller GLS
* Phillips-Perron
* KPSS
* Zivot-Andrews
* Variance Ratio tests
- Augmented Dickey-Fuller
- Dickey-Fuller GLS
- Phillips-Perron
- KPSS
- Zivot-Andrews
- Variance Ratio tests

See the [unit root testing example notebook](http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/unitroot_examples.ipynb) for examples of testing series for unit roots.

<a id="bootstrap"></a>

### Bootstrap

* Bootstraps
* IID Bootstrap
* Stationary Bootstrap
* Circular Block Bootstrap
* Moving Block Bootstrap
* Methods
* Confidence interval construction
* Covariance estimation
* Apply method to estimate model across bootstraps
* Generic Bootstrap iterator

See the [bootstrap example notebook](http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/bootstrap_examples.ipynb)
- Bootstraps
- IID Bootstrap
- Stationary Bootstrap
- Circular Block Bootstrap
- Moving Block Bootstrap
- Methods
- Confidence interval construction
- Covariance estimation
- Apply method to estimate model across bootstraps
- Generic Bootstrap iterator

See the [bootstrap example notebook](http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/bootstrap_examples.ipynb)
for examples of bootstrapping the Sharpe ratio and a Probit model from statsmodels.

```python
Expand Down Expand Up @@ -151,37 +151,37 @@ ci = bs.conf_int(sharpe_ratio, 1000, method='percentile')

### Multiple Comparison Procedures

* Test of Superior Predictive Ability (SPA), also known as the Reality
Check or Bootstrap Data Snooper
* Stepwise (StepM)
* Model Confidence Set (MCS)
- Test of Superior Predictive Ability (SPA), also known as the Reality
Check or Bootstrap Data Snooper
- Stepwise (StepM)
- Model Confidence Set (MCS)

See the [multiple comparison example notebook](http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/multiple-comparison_examples.ipynb)
for examples of the multiple comparison procedures.

## Requirements

These requirements reflect the testing environment. It is possible
These requirements reflect the testing environment. It is possible
that arch will work with older versions.

* Python (3.6+)
* NumPy (1.14+)
* SciPy (1.0.1+)
* Pandas (0.23+)
* statsmodels (0.9+)
* matplotlib (2.0+), optional
* property-cached (1.6.3+), optional
- Python (3.6+)
- NumPy (1.14+)
- SciPy (1.0.1+)
- Pandas (0.23+)
- statsmodels (0.9+)
- matplotlib (2.0+), optional
- property-cached (1.6.3+), optional

### Optional Requirements

* Numba (0.35+) will be used if available **and** when installed using the --no-binary option
* jupyter and notebook are required to run the notebooks
- Numba (0.35+) will be used if available **and** when installed using the --no-binary option
- jupyter and notebook are required to run the notebooks

## Installing

Standard installation with a compiler requires Cython. If you do not
have a compiler installed, the `arch` should still install. You will
see a warning but this can be ignored. If you don't have a compiler,
see a warning but this can be ignored. If you don't have a compiler,
`numba` is strongly recommended.

### pip
Expand Down Expand Up @@ -219,24 +219,24 @@ conda install arch -c bashtage
### Windows

Building extension using the community edition of Visual Studio is
well supported for Python 3.6+. Building on other combinations of
Python/Windows is more difficult and is not necessary when numba
well supported for Python 3.6+. Building on other combinations of
Python/Windows is more difficult and is not necessary when numba
is installed since just-in-time compiled code (numba) runs as fast as
ahead-of-time compiled extensions.

### Developing

The development requirements are:

* Cython (0.29+, if not using --no-binary)
* pytest (For tests)
* sphinx (to build docs)
* sphinx_material (to build docs)
* jupyter, notebook and nbsphinx (to build docs)
- Cython (0.29+, if not using --no-binary)
- pytest (For tests)
- sphinx (to build docs)
- sphinx_material (to build docs)
- jupyter, notebook and nbsphinx (to build docs)

### Installation Notes

1. If Cython is not installed, the package will be installed
as-if `--no-binary` was used.
2. Setup does not verify these requirements. Please ensure these are
2. Setup does not verify these requirements. Please ensure these are
installed.
14 changes: 9 additions & 5 deletions doc/source/changes/4.0.txt
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Expand Up @@ -2,13 +2,17 @@
Version 4
=========

Since 4.11
==========
Release 4.12
============
- Added typing support to all classes, functions and methods (:issue:`338`,
:issue:`341`, :issue:`342`, :issue:`343`, :issue:`345`, :issue:`346`).
- Fixed an issue that caused tests to fail on SciPy 1.4+ (:issue:`339`).
- Dropped support for Python 3.5 inline with NEP 29 (:issue:`334`).
- Added methods to compute moment and lower partial moments for standardized residuals. See,
for example, :func:`~arch.univariate.SkewStudent.moment` and
- Added methods to compute moment and lower partial moments for standardized
residuals. See, for example, :func:`~arch.univariate.SkewStudent.moment` and
:func:`~arch.univariate.SkewStudent.partial_moment` (:issue:`329`).
- Fixed a bug that produced an OverflowError when a time series has no variance (:issue:`331`).
- Fixed a bug that produced an OverflowError when a time series has no
variance (:issue:`331`).

Release 4.11
============
Expand Down

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