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Power issues when testing the Markov switching model with the sup likelihood ratio test using U.S. output

Author

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  • Patrick J. Coe

    (Department of Economics, University of Calgary, 2500 University Drive N.W., Calgary, AB Canada T2N 1N4)

Abstract

The likelihood ratio (LR) test statistic for the test of a linear AR(1) model against the alternative of a Markov switching model does not possess the standard 2 distribution. Garcia (1998) derives the asymptotic distribution of the Sup LR test statistic under these non-standard conditions allowing the researcher to easily compare the two models. This paper examines the power properties of this test statistic using Monte Carlo experiments calibrated to U.S. output growth data. The results suggest a test of reasonable power. When the experiments are calibrated to annual data, power is 82% at 200 observations. When the experiments are calibrated to quarterly data power is 57% for the same sample size.

Suggested Citation

  • Patrick J. Coe, 2002. "Power issues when testing the Markov switching model with the sup likelihood ratio test using U.S. output," Empirical Economics, Springer, vol. 27(2), pages 395-401.
  • Handle: RePEc:spr:empeco:v:27:y:2002:i:2:p:395-401
    Note: Received: March 2000/Final Version Received: March 2001
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    Cited by:

    1. Apostolos Thomadakis, 2012. "Contagion or Flight-to-Quality Phenomena in Stock and Bond Returns," School of Economics Discussion Papers 0612, School of Economics, University of Surrey.
    2. Cheung, Yin-Wong & Erlandsson, Ulf G., 2005. "Exchange Rates and Markov Switching Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 314-320, July.

    More about this item

    Keywords

    Markov switching model; power of sup LR test;

    JEL classification:

    • 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

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