English, Article, Journal or magazine article edition: Bayesian Inference on Dynamic Models with Latent Factors Monica Billio; Roberto Casarin; Domenico Sartore

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Bookmark: http://trove.nla.gov.au/version/203678
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Bayesian Inference on Dynamic Models with Latent Factors
Author
  • Monica Billio
  • Roberto Casarin
  • Domenico Sartore
Physical Description
  • preprint
Notes
  • 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.
  • Bayesian Dynamic Models, Simulation Based Inference, Particle Filters, Latent Factors, Business Cycle
  • RePEc:ven:wpaper:2007_34
Language
  • English
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