Markov-Switching Quantile Autoregression

Xiaochun Liu ()

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Asymmetric-Laplace Distribution; Metropolis-Hastings; Block-at-a-Time; Asymmetric Dynamics; Transition Probability (search for similar items in EconPapers)
JEL-codes: C51 C58 E0 E3 E32 G1 (search for similar items in EconPapers)
Date: 2013-10-07
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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https://mpra.ub.uni-muenchen.de/55800/1/MPRA_paper_55800.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/64662/9/MPRA_paper_64662.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/67276/1/MPRA_paper_67276.pdf revised version (application/pdf)

Related works:
Journal Article: Markov switching quantile autoregression (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:55800

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