SVAR-MSH-ID

R Code for Bayesian Inference for Structural Vector Autoregressions Identified with Markov-Switching Heteroskedasticity

by Tomasz Woźniak

Summary. The project provides source code and reproduction files for the paper mentioned below. Utility functions for the Gibbs sampler for Bayesian Structural Vector Autoregressions with Markov-Switching Heteroskedasticity, Savage-Dickey density ratio for uniqueness conditions and homoskedasticity hypothesis, and a marginal data density estimator are provided.

Keywords. SVAR-MSH, identification through heteroskedasticity, Savage-Dickey density ratio for uniquenss conditions, Gibbs sampler

Citation

To refer to the code in publications, please, cite the following paper:

Lütkepohl, H., Woźniak, T. (2020) Bayesian Inference for Structural Vector Autoregressions Identified with Markov-Switching Heteroskedasticity, Journal of Economic Dynamics and Control, 113, DOI: 10.1016/j.jedc.2020.103862.

Corrigendum

A correct version of the first equation on the top of page 17 from the Appendix of the paper is given by:

Project contents

The project includes:

• folder code with the whole source code
• R files for the reproduction of most of the results from the paper
• file dataBI2015.RData with data used in the paper

Downloading the code

To download the code simply click on the download icon on the top of this page

and select the format of the compressed file to be downloaded.

Forking and contributing

You can also choose to fork the project and clone your copy from the repository. Just follow the steps (requires a GitLab account):

1. On this page: fork the project by clicking the icon on the top of this page (more info)

   

2. On you computer: clone the repository you have just forked to create its local copy that you can work with.

3. Optional: if you find a bug or if you improve the code, please feel free to submit a pull/merge request. (more info)