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AN N-STATE ENDOGENOUS MARKOV-SWITCHING MODEL WITH APPLICATIONS IN MACROECONOMICS AND FINANCE

Published online by Cambridge University Press:  23 December 2019

Shih-Tang Hwu ,
Chang-Jin Kim  and
Jeremy Piger
Shih-Tang Hwu
Affiliation:
California State Polytechnic University
Chang-Jin Kim
Affiliation:
University of Washington
Jeremy Piger*
Affiliation:
University of Oregon
*
Address correspondence to: Jeremy Piger, Department of Economics, 1285 University of Oregon, Eugene, OR97403, USA. e-mail: [email protected]. Phone: 541-346-6075.
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Abstract

We develop an N-regime Markov-switching model in which the latent state variable driving the regime switching is endogenously determined with the model disturbance term. The models structure captures a wide variety of patterns of endogeneity and yields a simple test of the null hypothesis of exogenous switching. We derive an iterative filter that generates objects of interest, including the model likelihood function and estimated regime probabilities. Using simulation experiments, we demonstrate that the maximum likelihood estimator performs well in finite samples and that a likelihood ratio test of exogenous switching has good size and power properties. We provide results from two applications of the endogenous switching model: a three-state model of US business cycle dynamics and a three-state volatility model of US equity returns. In both cases, we find statistically significant evidence in favor of endogenous switching.

Keywords

Regime SwitchingBusiness Cycle AsymmetryNonlinear ModelsVolatility FeedbackEquity Premium
Type
Articles
Information
Macroeconomic Dynamics , Volume 25 , Issue 8 , December 2021 , pp. 1937 - 1965
Copyright
© Cambridge University Press 2019

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Footnotes

We thank seminar participants at the Federal Reserve Bank of St. Louis, the University of Oregon, the University of Washington, the University of California, Santa Barbara, and the 2017 ASSA meetings for helpful comments. Previous versions of this paper circulated under the title N-State Endogenous Markov-Switching Models.

References

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