Quotation Malsiner-Walli, Gertraud, Hofmarcher, Paul, Grün, Bettina. 2019. Semi‐parametric Regression under Model Uncertainty: Economic Applications. Oxford Bulletin of Economics and Statistics. 81 (5), 1117-1143.




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.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Oxford Bulletin of Economics and Statistics
Citation Index SSCI
WU Journalrating 2009 A
WU-Journal-Rating new FIN-A, VW-C, WH-B
Language English
Title Semi‐parametric Regression under Model Uncertainty: Economic Applications
Volume 81
Number 5
Year 2019
Page from 1117
Page to 1143
Reviewed? Y
DOI https://doi.org/10.1111/obes.12294
Open Access Y
Open Access Link https://onlinelibrary.wiley.com/doi/full/10.1111/obes.12294


Shrinking and Regularizing Finite Mixture Models
Malsiner-Walli, Gertraud (Details)
Grün, Bettina (Johannes Kepler Universtät Linz, Austria)
Hofmarcher, Paul (Paris Lodron University of Salzburg, Austria)
Institute for Statistics and Mathematics IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1162 Statistics (Details)
5701 Applied statistics (Details)
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