A Comparison of Two Averaging Techniques with an Application to Growth Empirics

J.R. Magnus, O.R. Powell, P. Prüfer

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Abstract

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.
Original languageEnglish
Place of PublicationTilburg
PublisherEconometrics
Number of pages42
Volume2008-39
Publication statusPublished - 2008

Publication series

NameCentER Discussion Paper
Volume2008-39

Keywords

  • Model averaging
  • Bayesian analysis
  • Growth determinants

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