Bayesian Inference for Markov-switching Skewed Autoregressive Models
Stéphane Lhuissier ()
Working papers from Banque de France
We examine Markov-switching autoregressive models where the commonly used Gaussian assumption for disturbances is replaced with a skew-normal distribution. This allows us to detect regime changes not only in the mean and the variance of a specified time series, but also in its skewness. A Bayesian framework is developed based on Markov chain Monte Carlo sampling. Our informative prior distributions lead to closed-form full conditional posterior distributions, whose sampling can be efficiently conducted within a Gibbs sampling scheme. The usefulness of the methodology is illustrated with a real-data example from U.S. stock markets.
Keywords: Regime switching; Skewness; Gibbs-sampler; time series analysis; upside and downside risks. (search for similar items in EconPapers)
JEL-codes: C01 C11 C2 G11 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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