Asymptotic properties of the maximum likelihood estimator in regime switching econometric models

Hiroyuki Kasahara and Katsumi Shimotsu

Journal of Econometrics, 2019, vol. 208, issue 2, 442-467

Abstract: Markov regime switching models have been widely used in numerous empirical applications in economics and finance. However, the asymptotic distribution of the maximum likelihood estimator (MLE) has not been proven for some empirically popular Markov regime switching models. In particular, the asymptotic distribution of the MLE has been unknown for models in which some elements of the transition probability matrix have the value of zero, as is commonly assumed in empirical applications with models with more than two regimes. This also includes models in which the regime-specific density depends on both the current and the lagged regimes such as the seminal model of Hamilton (1989) and switching ARCH model of Hamilton and Susmel (1994). This paper shows the asymptotic normality of the MLE and consistency of the asymptotic covariance matrix estimate of these models.

Keywords: Asymptotic distribution; Autoregressive conditional heteroscedasticity; Maximum likelihood estimator; Markov regime switching (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Related works:
Working Paper: Asymptotic Properties of the Maximum Likelihood Estimator in Regime Switching Econometric Models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:208:y:2019:i:2:p:442-467

DOI: 10.1016/j.jeconom.2018.09.019

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