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.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Bayes, Metropolis-Hastings, Particle filter, Unobserved components
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling

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