Metadata-Version: 2.4
Name: SVARpy
Version: 0.1.17
Summary: SVAR estimation
Home-page: https://github.com/Saschakew/SVARpy
Author: Sascha Keweloh
Author-email: sascha.keweloh@tu-dortmund.de
License: MIT License
        
        Copyright (c) 2020 Sascha Keweloh
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: license-file
Dynamic: requires-python
Dynamic: summary

# SVARpy

The SVARpy Python package aims to provide easy and quick access to the non-Gaussian moment-based estimators proposed in the following studies:

- Keweloh, Sascha Alexander. "A generalized method of moments estimator for structural vector autoregressions based on higher moments." Journal of Business & Economic Statistics 39.3 (2021): 772-782.

- Keweloh, Sascha Alexander, Stephan Hetzenecker, and Andre Seepe. "Monetary Policy and Information Shocks in a Block-Recursive SVAR." Journal of International Money and Finance (2023).

- Keweloh, Sascha Alexander. "A feasible approach to incorporate information in higher moments in structural vector autoregressions." (2021).

## Quick Install

To install SVARpy, use the following command:

> pip install SVARpy


## Overview

The [SVARpyExamples](https://github.com/Saschakew/SVARpyExamples) repository on GitHub contains notebooks providing a brief overview of the main functionalities of the package.

The notebooks can be accessed online:

1. **Intuition**: 
This notebook visualizes how leveraging dependency measures based on covariance, coskewness, and cokurtosis can be used to estimate a non-Gaussian SVAR.
[Notebook](https://colab.research.google.com/github/Saschakew/SVARpyExamples/blob/main/SVARpy-Intuition.ipynb)

2. **SVAR-GMM**: 
Overview on the implementation of the SVAR-GMM method in Keweloh (2021).
[Notebook](https://colab.research.google.com/github/Saschakew/SVARpyExamples/blob/main/SVARpy-SVARGMM.ipynb)

3. **Fast SVAR-GMM**: 
Overview on the implementation of the fast SVAR-GMM method in Keweloh (2021).
[Notebook](https://colab.research.google.com/github/Saschakew/SVARpyExamples/blob/main/SVARpy-SVARGMMfast.ipynb)

4. **SVAR-CUE**: 
Overview on the implementation of the continuous updating version of the SVAR-GMM method in Keweloh (2021).
[Notebook](https://colab.research.google.com/github/Saschakew/SVARpyExamples/blob/main/SVARpy-SVARCUE.ipynb)

5. **Block-Recursive SVAR**: 
Overview on how to pass block-recursive restrictions to the estimator, see Keweloh et al. (2023).
[Notebook](https://colab.research.google.com/github/Saschakew/SVARpyExamples/blob/main/SVARpy-SVARGMM-BlockRec.ipynb)

## References

Keweloh, Sascha Alexander. "A generalized method of moments estimator for structural vector autoregressions based on higher moments." Journal of Business & Economic Statistics 39.3 (2021): 772-782.

Keweloh, Sascha Alexander. "A feasible approach to incorporate information in higher moments in structural vector autoregressions." (2021b).

Keweloh, Sascha A., Stephan Hetzenecker, and Andre Seepe. "Monetary Policy and Information Shocks in a Block-Recursive SVAR." Journal of International Money and Finance (2023).