Gibbs sampling in econometric practice

Michiel De Pooter, Rene Segers () and Herman van Dijk

No EI 2006-13, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: We present a road map for effective application of Bayesian analysis of a class of well-known dynamic econometric models by means of the Gibbs sampling algorithm. Members belonging to this class are the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model and as Hierarchical Linear Mixed Models, the State-Space model and the Panel Data model. We discuss issues involved when drawing Bayesian inference on equation parameters and variance components and show that one should carefully scan the shape of the criterion function for irregularities before applying the Gibbs sampler. Analytical, graphical and empirical results are used along the way.

Keywords: Gibbs sampler; MCMC; non-stationarity; random effects panel date models; reduced rank models; serial correlation; state-space models (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 C23 C30 (search for similar items in EconPapers)
Date: 2006-03-21
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://repub.eur.nl/pub/7743/EI%20report%202006-13.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ems:eureir:7743

Access Statistics for this paper

More papers in Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute Contact information at EDIRC.
Bibliographic data for series maintained by RePub ( this e-mail address is bad, please contact ).