Semi-parametric regression under model uncertainty: Economic applications.

Paul Hofmarcher, Bettina Grün, Gertraud Malsiner-Walli

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)1117-1143
Number of pages27
JournalOxford Bulletin of Economics and Statistics
Volume81
Issue number5
DOIs
Publication statusPublished - 2019

Bibliographical note

© 2019 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and JohnWiley & Sons Ltd.

Fields of Science and Technology Classification 2012

  • 502 Economics

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