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Remarks on Foundations of Bayesian Statistics and Econometrics

  • Wolfgang Polasek

Abstract

Econometrics can be viewed as applying statistics in economics. Applied statistics is a toolbox of coherent methods to deal with empirical uncertainties. Therefore a reasonable conjecture is that any Bayesian statistical method is applicable to some economic problem. Statistical methods in econometrics are breaking new grounds in two areas with very specific problems: a) the non-experimental nature of almost all economic data, and b) simultaneous equation systems.

For this reason we concentrate on foundational statistical issues in the first part of the paper and switch to a brief survey on new econometric developments in the second part. The review follows the “search approach” to econometrics, proposed for non-experimental data by Learner (1978). This includes robust Bayesian methods, or in more fashionable term the extreme bound analysis (EBA), hierarchical models, smoothness priors for multivariate time series models, and Bayesian regression diagnostics. Furthermore, we review recent developments of numerical integration techniques (importance functions) in Bayesian simultaneous equation systems. Finally we discuss the acceptance of Bayesian methods in econometrics and possible future developments.

Keywords

Bayesian Inference Bayesian Statistics Simultaneous Equation Model Importance Function Local Sensitivity Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press, New York 1987

Authors and Affiliations

  • Wolfgang Polasek
    • 1
  1. 1.Institute for StatisticsUniversity of ViennaViennaAustria

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