A comparison of two model averaging techniques with an application to growth empirics
نویسنده
چکیده
Empirical growth research faces a high degree of model uncertainty. The current paper deals with the fundamental issue of parameter estimation under model uncertainty, and 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. JEL Classification: C51, C52, C13, C11
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