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Identification-robust moment-based tests for Markov-switching in autoregressive models

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  • Jean-Marie Dufour
  • Richard Luger

Abstract

This paper develops tests of the null hypothesis of linearity in the context of autoregressive models with Markov-switching means and variances. These tests are robust to the identification failures that plague conventional likelihood-based inference methods. The approach exploits the moments of normal mixtures implied by the regime-switching process and uses Monte Carlo test techniques to deal with the presence of an autoregressive component in the model specification. The proposed tests have very respectable power in comparison to the optimal tests for Markov-switching parameters of Carrasco et al. (2014) and they are also quite attractive owing to their computational simplicity. The new tests are illustrated with an empirical application to an autoregressive model of U.S. output growth.

Suggested Citation

  • Jean-Marie Dufour & Richard Luger, 2016. "Identification-robust moment-based tests for Markov-switching in autoregressive models," CIRANO Working Papers 2016s-63, CIRANO.
    • Handle: RePEc:cir:cirwor:2016s-63
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    File URL: https://cirano.qc.ca/files/publications/2016s-63.pdf
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    References listed on IDEAS

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    Cited by:

    1. Hiroyuki Kasahara & Katsumi Shimotsu, 2018. "Testing the Number of Regimes in Markov Regime Switching Models," Papers 1801.06862, arXiv.org, revised Jan 2018.
    2. Gabriel Rodriguez-Rondon & Jean-Marie Dufour, 2024. "MSTest: An R-Package for Testing Markov Switching Models," Papers 2411.08188, arXiv.org.

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    More about this item

    Keywords

    Mixture distributions; Markov chains; Regime switching; Parametric bootstrap; Monte Carlo tests; Exact inference;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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