%0 Generic
%T A comparison of two model averaging techniques with an application to growth empirics
%A Magnus, Jan R.
%A Powell, Owen
%A Prüfer, Patricia
%K C51
%K C52
%K C13
%K C11
%K Model averaging
%K Bayesian analysis
%K Growth determinants
%D 2009
%X Postprint
%X begutachtet (peer reviewed)
%X In: Journal of Econometrics ; 154 (2009) 2 ; 139-153
%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.
%U http://slubdd.de/katalog?TN_libero_mab2
Download citation