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

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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

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Series:	Computing in Economics and Finance 2004.
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:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005345329