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

  • Magnus, Jan R.
  • Powell, Owen
  • Prüfer, Patricia

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.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 154 (2010)
Issue (Month): 2 (February)
Pages: 139-153

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Handle: RePEc:eee:econom:v:154:y:2010:i:2:p:139-153
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