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

Listed author(s):
  • Nima Nonejad

    ()

    (Aarhus University and CREATES)

Registered author(s):

    This paper details Particle Markov chain Monte Carlo techniques for analysis of unobserved component time series models using several economic data sets. PMCMC combines the particle filter with the Metropolis-Hastings algorithm. Overall PMCMC provides a very compelling, computationally fast and efficient framework for estimation. These advantages are used to for instance estimate stochastic volatility models with leverage effect or with Student-t distributed errors. We also model changing time series characteristics of the US inflation rate by considering a heteroskedastic ARFIMA model where the heteroskedasticity is specified by means of a Gaussian stochastic volatility process.

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    File URL: ftp://ftp.econ.au.dk/creates/rp/13/rp13_27.pdf
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    Paper provided by Department of Economics and Business Economics, Aarhus University in its series CREATES Research Papers with number 2013-27.

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    Length: 21
    Date of creation: 08 2013
    Handle: RePEc:aah:create:2013-27
    Contact details of provider: Web page: http://www.econ.au.dk/afn/

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    1. Grassi Stefano & Proietti Tommaso, 2010. "Has the Volatility of U.S. Inflation Changed and How?," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-22, September.
    2. Flury, Thomas & Shephard, Neil, 2011. "Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models," Econometric Theory, Cambridge University Press, vol. 27(05), pages 933-956, October.
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