MSTest: An R-Package for Testing Markov Switching Models

TL;DR

The paper introduces MSTest, an R package that provides comprehensive hypothesis testing, simulation, and estimation tools for Markov switching models, facilitating analysis in economics and finance.

Contribution

It offers an integrated R package implementing multiple hypothesis tests, simulation, and estimation methods for Markov switching models, enhancing research capabilities.

Findings

Successfully applied to U.S. GNP growth data

Demonstrated through simulation experiments

Includes multiple hypothesis testing procedures

Abstract

We present the R package MSTest, which implements hypothesis testing procedures to identify the number of regimes in Markov switching models. These models have wide-ranging applications in economics, finance, and numerous other fields. The MSTest package includes the Monte Carlo likelihood ratio test procedures proposed by Rodriguez-Rondon and Dufour (2024), the moment-based tests of Dufour and Luger (2017), the parameter stability tests of Carrasco, Hu, and Ploberger (2014), and the likelihood ratio test of Hansen (1992). Additionally, the package enables users to simulate and estimate univariate and multivariate Markov switching and hidden Markov processes, using the expectation-maximization (EM) algorithm or maximum likelihood estimation (MLE). We demonstrate the functionality of the MSTest package through both simulation experiments and an application to U.S. GNP growth data.