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%T A comparison of two model averaging techniques with an application to growth empirics %A Magnus, Jan R. %A Powell, Owen %A Prüfer, Patricia %J Journal of Econometrics %N 2 %P 139-153 %V 154 %D 2009 %K C51; C52; C13; C11; Model averaging; Bayesian analysis; Growth determinants %= 2011-08-08T09:31:00Z %~ http://www.peerproject.eu/ %> https://nbn-resolving.org/urn:nbn:de:0168-ssoar-262608 %X 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. %C NLD %G en %9 journal article %W GESIS - http://www.gesis.org %~ SSOAR - http://www.ssoar.info