Learning the Shape of the Likelihood of Typical Econometric Models using Gibbs Sampling
Michiel De Pooter and
Rene Segers ()
No 82, Computing in Economics and Finance 2004 from Society for Computational Economics
The shape of the likelihood of several recently developed econometric models is often non-elliptical. Learning this shape using Gibbs sampling is discussed in this paper. A systematic analysis using graphical and computational methods is presented. Examples of the models considered in this paper are nearly non-stationary and non-identified models, weak-instrument models, mixture models and random-coefficients panel-data models
Keywords: Gibbs sampler; MCMC; non-stationarity; reduced rank models; label switching; random coefficients panel data models (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 (search for similar items in EconPapers)
References: Add references at CitEc
Citations: Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf4:82
Access Statistics for this paper
More papers in Computing in Economics and Finance 2004 from Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().