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Semi‐parametric Regression under Model Uncertainty: Economic Applications

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  • Gertraud Malsiner‐Walli
  • Paul Hofmarcher
  • Bettina Grün

Abstract

Economic theory does not always specify the functional relationship between dependent and explanatory variables, or even isolate a particular set of covariates. This means that model uncertainty is pervasive in empirical economics. In this paper, we indicate how Bayesian semi‐parametric regression methods in combination with stochastic search variable selection can be used to address two model uncertainties simultaneously: (i) the uncertainty with respect to the variables which should be included in the model and (ii) the uncertainty with respect to the functional form of their effects. The presented approach enables the simultaneous identification of robust linear and nonlinear effects. The additional insights gained are illustrated on applications in empirical economics, namely willingness to pay for housing, and cross‐country growth regression.

Suggested Citation

  • Gertraud Malsiner‐Walli & Paul Hofmarcher & Bettina Grün, 2019. "Semi‐parametric Regression under Model Uncertainty: Economic Applications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 1117-1143, October.
    • Handle: RePEc:bla:obuest:v:81:y:2019:i:5:p:1117-1143
    DOI: 10.1111/obes.12294
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    Cited by:

    1. Bettina Grün & Paul Hofmarcher, 2021. "Identifying groups of determinants in Bayesian model averaging using Dirichlet process clustering," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 1018-1045, September.

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