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Gibbs sampling in econometric practice

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  • de Pooter, M.D.
  • Segers, R.
  • van Dijk, H.K.

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.

Suggested Citation

  • de Pooter, M.D. & Segers, R. & van Dijk, H.K., 2006. "Gibbs sampling in econometric practice," Econometric Institute Research Papers EI 2006-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:7743
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Michiel D. de Pooter & René Segers & Herman K. van Dijk, 2006. "On the Practice of Bayesian Inference in Basic Economic Time Series Models using Gibbs Sampling," Tinbergen Institute Discussion Papers 06-076/4, Tinbergen Institute.
    2. Munch, Jakob R. & Nguyen, Daniel X., 2014. "Decomposing firm-level sales variation," Journal of Economic Behavior & Organization, Elsevier, vol. 106(C), pages 317-334.
    3. Michiel de Pooter & Francesco Ravazzolo & Rene Segers & Herman K. van Dijk, 2008. "Bayesian near-boundary analysis in basic macroeconomic time-series models," Advances in Econometrics, in: Bayesian Econometrics, pages 331-402, Emerald Group Publishing Limited.

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    More about this item

    Keywords

    Gibbs sampler; MCMC; non-stationarity; random effects panel date models; reduced rank models; serial correlation; state-space 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
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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