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Learning the Shape of the Likelihood of Typical Econometric Models using Gibbs Sampling

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
Year of publication:
2004-08-11
Authors: Pooter, Michiel D. de ; Segers, Rengert
Institutions: Society for Computational Economics - SCE
Subject: Gibbs sampler | MCMC | non-stationarity | reduced rank models | label switching | random coefficients panel data models
Series:
Type of publication: Book / Working Paper
Notes:
The text is part of a series Computing in Economics and Finance 2004 Number 82
Classification: C11 - Bayesian Analysis ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C22 - Time-Series Models
Source:
Persistent link: https://www.econbiz.de/10005345329
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