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Markov switching quantile autoregression

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  • Xiaochun Liu

Abstract

This paper considers the location-scale quantile autoregression in which the location and scale parameters are subject to regime shifts. The regime changes are determined by the outcome of a latent, discrete-state Markov process. The new method provides direct inference and estimate for different parts of a nonstationary time series distribution. Bayesian inference for switching regimes within a quantile,via a three-parameter asymmetric-Laplace distribution, is adapted and designed for parameter estimation. The simulation study shows reasonable accuracy and precision in model estimation. From a distribution point of view, rather than from a mean point of view, the potential of this new approach is illustrated in the empirical applications to reveal the countercyclical risk pattern of stock markets and the asymmetric persistence of real GDP growth rates and real trade-weighted exchange rates.
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  • Xiaochun Liu, 2016. "Markov switching quantile autoregression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 356-395, November.
    • Handle: RePEc:bla:stanee:v:70:y:2016:i:4:p:356-395
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    Cited by:

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    5. Xiaochun Liu, 2018. "How is the Taylor Rule Distributed under Endogenous Monetary Regimes?," International Review of Finance, International Review of Finance Ltd., vol. 18(2), pages 305-316, June.

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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E0 - Macroeconomics and Monetary Economics - - General
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G1 - Financial Economics - - General Financial Markets

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