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Semi-parametric Regression under Model Uncertainty: Economic Applications.

Gertraud Malsiner-Walli1, Paul Hofmarcher2, Bettina Grün3

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Oxford Bulletin of Economics and Statistics
|November 22, 2019
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Summary
This summary is machine-generated.

This study introduces Bayesian semi-parametric regression and stochastic search variable selection to tackle model uncertainty in economics. The methods simultaneously identify relevant variables and their functional forms, revealing robust linear and nonlinear effects.

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Area of Science:

  • Econometrics
  • Statistical Modeling

Background:

  • Model uncertainty is a significant challenge in empirical economics due to unspecified functional relationships and covariate sets.
  • Traditional methods often struggle to simultaneously address uncertainty in variable selection and functional form specification.

Purpose of the Study:

  • To present a Bayesian semi-parametric regression approach combined with stochastic search variable selection.
  • To address two key sources of model uncertainty: variable inclusion and functional form specification.
  • To simultaneously identify robust linear and nonlinear effects in economic models.

Main Methods:

  • Bayesian semi-parametric regression analysis.
  • Stochastic search variable selection (SSVS).
  • Simultaneous modeling of variable selection and functional form uncertainty.

Main Results:

  • The proposed methodology effectively handles pervasive model uncertainty in empirical economics.
  • It allows for the simultaneous identification of both linear and nonlinear relationships.
  • Demonstrated utility in real-world economic applications like willingness to pay and cross-country growth.

Conclusions:

  • Bayesian semi-parametric methods with SSVS offer a powerful framework for robust economic modeling.
  • This approach enhances the reliability of empirical findings by accounting for model uncertainty.
  • The methods provide deeper insights into economic relationships than traditional techniques.