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


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


Bitte beziehen Sie sich beim Zitieren dieses Dokumentes immer auf folgenden Persistent Identifier (PID):http://nbn-resolving.de/urn:nbn:de:0168-ssoar-262608

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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.
Klassifikation Wirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatik
Freie Schlagwörter C51; C52; C13; C11; Model averaging; Bayesian analysis; Growth determinants
Sprache Dokument Englisch
Publikationsjahr 2009
Seitenangabe S. 139-153
Zeitschriftentitel Journal of Econometrics, 154 (2009) 2
DOI http://dx.doi.org/10.1016/j.jeconom.2009.07.004
Status Postprint; begutachtet (peer reviewed)
Lizenz PEER Licence Agreement (applicable only to documents from PEER project)