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Bayesian Inference on Dynamic Models with Latent Factors

  • Monica Billio

    ()

    (Department of Economics, University Of Venice Cà Foscari)

  • Roberto Casarin

    (University of Brescia)

  • Domenico Sartore

    (Department of Economics, University Of Venice Cà Foscari)

In time series analysis, latent factors are often introduced to model the heterogeneous time evolution of the observed processes. The presence of unobserved components makes the maximum likelihood estimation method more difficult to apply. A Bayesian approach can sometimes be preferable since it permits to treat general state space models and makes easier the simulation based approach to parameters estimation and latent factors filtering. The paper examines economic time series models in a Bayesian perspective focusing, through some examples, on the extraction of the business cycle components. We briefly review some general univariate Bayesian dynamic models and discuss the simulation based techniques, such as Gibbs sampling, adaptive importance sampling and finally suggest the use of the particle filter, for parameter estimation and latent factor extraction.

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File URL: http://www.unive.it/media/allegato/DIP/Economia/Working_papers/Working_papers_2007/WP_DSE_billio_casarin_sartore_34_07.pdf
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Paper provided by Department of Economics, University of Venice "Ca' Foscari" in its series Working Papers with number 2007_34.

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Length: 20
Date of creation: 2007
Date of revision:
Handle: RePEc:ven:wpaper:2007_34
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  1. Jacques Anas & Laurent Ferrara, 2004. "Detecting Cyclical Turning Points: The ABCD Approach and Two Probabilistic Indicators," Journal of Business Cycle Measurement and Analysis, OECD Publishing,Centre for International Research on Economic Tendency Surveys, vol. 2004(2), pages 193-225.
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