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

Nonejad, Nima (2014): Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox.

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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.

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