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A comparison of two model averaging techniques with an application to growth empirics

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Magnus, Jan R.; Powell, Owen; Prüfer, Patricia

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Abstract Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. This paper compares the performance of various model averaging techniques. In particular, it contrasts Bayesian model averaging (BMA) — currently one of the standard methods used in growth empirics — with a new method called weighted-average least squares (WALS). The new method has two major advantages over BMA: its computational burden is trivial and it is based on a transparent definition of prior ignorance. The theory is applied to and sheds new light on growth empirics where a high degree of model uncertainty is typically present.
Classification Economic Statistics, Econometrics, Business Informatics
Free Keywords C51; C52; C13; C11; Model averaging; Bayesian analysis; Growth determinants
Document language English
Publication Year 2009
Page/Pages p. 139-153
Journal Journal of Econometrics, 154 (2009) 2
Status Postprint; peer reviewed
Licence PEER Licence Agreement (applicable only to documents from PEER project)