Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86236
Authors: 
de Pooter, Michiel D.
Segers, René
van Dijk, Herman K.
Year of Publication: 
2006
Series/Report no.: 
Tinbergen Institute Discussion Paper 06-076/4
Abstract: 
Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables 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 regression parameters and variance components, in particular when some parameter have substantial posterior probability near the boundary of the parameter region, and show that one should carefully scan the shape of the posterior density function. Analytical, graphical and empirical results are used along the way.
Subjects: 
Gibbs sampler
MCMC
serial correlation
non-stationarity
reduced rank models
state-space models
random effects panel data models
JEL: 
C11
C15
C22
C23
C30
Document Type: 
Working Paper

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