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

作者：Magnus Jan R^*; Powell Owen; Prufer Patricia

来源：Journal of Econometrics, 2010, 154(2): 139-153.

DOI：10.1016/j.jeconom.2009.07.004

• Model averaging
• Bayesian analysis
• Growth determinants

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 ingrowth 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.

• 出版日期2010-2