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

Magnus,J.R.
; 
Powell,O.R.
; 
Prüfer,P.
Abstract
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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Appeared earlier as CentER DP 2008-39
Date
2010
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Tilburg University Research Output Collection
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Magnus, J R, Powell, O R & Prüfer, P 2010, 'A comparison of two model averaging techniques with an application to growth empirics', Journal of Econometrics, vol. 154, pp. 139-153. https://doi.org/10.1016/j.jeconom.2009.07.004
URI
https://hdl.handle.net/20.500.14602/51390
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