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

Author

Listed:
  • Michiel D. de Pooter
  • Rengert Segers

Abstract

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

Suggested Citation

  • Michiel D. de Pooter & Rengert Segers, 2004. "Learning the Shape of the Likelihood of Typical Econometric Models using Gibbs Sampling," Computing in Economics and Finance 2004 82, Society for Computational Economics.
    • Handle: RePEc:sce:scecf4:82
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    More about this item

    Keywords

    Gibbs sampler; MCMC; non-stationarity; reduced rank models; label switching; random coefficients panel data models;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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