Bayesian Inference on Dynamic Models with Latent Factors

Monica Billio (), Roberto Casarin () and Domenico Sartore ()

No 2007_34, Working Papers from University of Venice "Ca' Foscari", Department of Economics

Abstract: 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.

Keywords: Bayesian Dynamic Models; Simulation Based Inference; Particle Filters; Latent Factors; Business Cycle (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 C63 O40 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2007
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