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

Nonejad Nima ()
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Nonejad Nima: Department of Economics and Business and Creates, Arhus University, Aarhus, Denmark

Journal of Time Series Econometrics, 2016, vol. 8, issue 1, 55-90

Abstract: This paper details particle Markov chain Monte Carlo (PMCMC) techniques for analysis of unobserved component time series models using several economic data sets. The objective of this paper is to explain the basics of the methodology and provide computational applications that justify applying PMCMC in practice. For instance, we use PMCMC to estimate a stochastic volatility model with a leverage effect, Student-t distributed errors or serial dependence. We also model time series characteristics of monthly US inflation rate by considering a heteroskedastic ARFIMA model where heteroskedasticity is specified by means of a Gaussian stochastic volatility process.

Keywords: Bayes; Gibbs; Metropolis–Hastings; particle filter; unobserved components (search for similar items in EconPapers)
JEL-codes: C11 C22 C63 (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1515/jtse-2013-0024

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