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Bayesian Semiparametric Inference in Multiple Equation Models

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  • Gary Koop

    ()

  • Dale Poirier
  • Justin Tobias

Abstract

This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components. The approach treats the points on each nonparametric regression line as unknown parameters and uses a prior on the degree of smoothness of each line to ensure valid posterior inference despite the fact that the number of parameters is greater than the number of observations. We derive an empirical Bayesian approach that allows us to estimate the prior smoothing hyperparameters from the data. An advantage of our semiparametric model is that it is written as a seemingly unrelated regressions model with independent Normal-Wishart prior. Since this model is a common one, textbook results for posterior inference, model comparison, prediction and posterior computation are immediately available. We use this model in an application involving a two-equation structural model drawn from the labor and returns to schooling literatures.

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Bibliographic Info

Paper provided by Department of Economics, University of Leicester in its series Discussion Papers in Economics with number 04/17.

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Date of creation: Jun 2003
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Handle: RePEc:lec:leecon:04/17

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Related research

Keywords: nonparametric regression; nonparametric instrumental variables; SUR model; endogeneity; nonlinear simultaneous equations;

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Cited by:
  1. Marianna Belloc & Ugo Pagano, 2008. "Politics-Business Interaction Paths," Working Papers 109, University of Rome La Sapienza, Department of Public Economics.

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