A Comparison of Two Averaging Techniques with an Application to Growth Empirics
CentER Discussion Paper Series No. 2008-39
42 Pages Posted: 17 Apr 2008
Date Written: April 3, 2008
Empirical growth research faces a high degree of model uncertainty. Apart from the neoclassical growth model, many new (endogenous) growth models have been proposed. This causes a lack of robustness of the parameter estimates and makes the determination of the key determinants of growth hazardous. 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 weighted-average least squares (WALS), a method that has not previously been applied in this context.
Keywords: model averaging, Bayesian analysis, growth determinants
JEL Classification: C51, C52, C13, C11
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