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

Jan R. Magnus , Owen Powell , Patricia Prüfer

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.

Mots clés: C51; C52; C13; C11; Model averaging; Bayesian analysis; Growth determinants

Jan R. Magnus, Owen Powell, Patricia Prüfer. A comparison of two model averaging techniques with an application to growth empirics. Econometrics, 2009, 154 (2), pp.139. ⟨10.1016/j.jeconom.2009.07.004⟩. ⟨hal-00613748⟩