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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 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

  2. SVAR-GMM: Overview on the implementation of the SVAR-GMM method in Keweloh (2021). Notebook

  3. Fast SVAR-GMM: Overview on the implementation of the fast SVAR-GMM method in Keweloh (2021). Notebook

  4. SVAR-CUE: Overview on the implementation of the continuous updating version of the SVAR-GMM method in Keweloh (2021). Notebook

  5. Block-Recursive SVAR: Overview on how to pass block-recursive restrictions to the estimator, see Keweloh et al. (2023). Notebook

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).

Release files for SVARpy 0.1.17

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

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Source distribution for SVARpy 0.1.17
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Total release size: 67.7 kB

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