TY - CHAP
TI - Bayesian Variable Selection Problems for State Space and Other Latent Variable Models
AB - Latent Variable models are widely used in applied statistics and econometrics to deal with data where the underlying processes change either over time or between units. Whereas estimation of these models is well understood, model selection problems are rarely studies, because such an issue usually leads to a non-regular testing problem.
Bayesian statistics offers in principle a framework for model selection even for non-regular problems, as is shortly discussed in the first part of the talk. The practical application of the Bayesian approach, however, proves to be challenging and numerical technique like marginal likelihoods, RJMCMC or the variable selection approach have to be used.
The main contribution of this talk is to demonstrate that the Bayesian variable selection approach is useful far beyond the common problem of selecting covariates in a classical regression model and may be extended to deal model selection problems in various latent variable models. First, it is extended to testing for the presence of unobserved heterogeneity in random effects models. Second, dynamic regression models are considered, where one has to choose between fixed and random coefficients. Finally, the variable selection approach is extended to state space models, where testing problems like discriminating between models with a stochastic trend, a deterministic trend and a model without trend arise.
Case studies from marketing, economics and finance will be considered for illustration.
AF - Center for Research in Statistical Methods (CRiSM), University of Warwick
PP - Warwick
UR - http://www2.warwick.ac.uk/fac/sci/statistics/crism/seminars/old-seminars
PY - 2008-01-01
AU - Frühwirth-Schnatter, Sylvia
ER -