Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox

Nima Nonejad

MPRA Paper from University Library of Munich, Germany

Abstract: This paper details particle Markov chain Monte Carlo (PMCMC) techniques for analysis of unobserved component time series models using several economic data sets. PMCMC provides a very compelling, computationally fast and efficient framework for estimation and model comparison. For instance, we estimate a stochastic volatility model with leverage effect and one with Student-t distributed errors. We also model time series characteristics of US inflation rate by considering a heteroskedastic ARFIMA model where heteroskedasticity is specified by means of a Gaussian stochastic volatility process.

Keywords: Bayes; Metropolis-Hastings; Particle filter; Unobserved components (search for similar items in EconPapers)
JEL-codes: C11 C22 C63 (search for similar items in EconPapers)
Date: 2014-05-01
New Economics Papers: this item is included in nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:55662

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