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Semi-parametric Regression under Model Uncertainty: Economic Applications

Malsiner-Walli, Gertraud and Hofmarcher, Paul and Grün, Bettina (2019) Semi-parametric Regression under Model Uncertainty: Economic Applications. Oxford Bulletin of Economics and Statistics. pp. 1-27. ISSN 0305-9049

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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.

Item Type: Article
Additional Information: This research was supported by the Oesterreichische Nationalbank (Anniversary Fund, project number: 14663) and the Austrian Science Fund (FWF, P28740).
Classification Codes: JEL Classification numbers: C14, O10, O40
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Version of the Document: Published
Depositing User: Gertraud Novotny
Date Deposited: 27 Feb 2019 08:20
Last Modified: 27 Feb 2019 13:55
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/90301/
URI: http://epub.wu.ac.at/id/eprint/6847

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