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Titel
Semiparametric Regression under Model Uncertainty : Economic Applications
VerfasserMalsinerWalli, Gertraud ; Hofmarcher, Paul ; Grün, Bettina
Erschienen in
Oxford Bulletin of Economics and Statistics, Hoboken, 2019, Jg. 2019, S. 1-27
ErschienenHoboken : Wiley, 2019
SpracheDeutsch
DokumenttypAufsatz in einer Zeitschrift
ISSN1468-0084
URNurn:nbn:at:at-ubs:3-11506 Persistent Identifier (URN)
DOI10.1111/obes.12294 
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Semiparametric Regression under Model Uncertainty [0.45 mb]
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Zusammenfassung (Englisch)

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 semiparametric 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 crosscountry growth regression.

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CC-BY-Lizenz (4.0)Creative Commons Namensnennung 4.0 International Lizenz