A comparison of two model averaging techniques with an application to growth empirics |
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Authors: | Jan R. Magnus, Owen Powell,Patricia Prü fer |
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Affiliation: | aDepartment of Econometrics & Operations Research, Tilburg University, The Netherlands;bCentER, Tilburg University, The Netherlands;cNetherlands Bureau for Economic Policy Analysis (CPB) and CentER, Tilburg University, The Netherlands |
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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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Keywords: | Model averaging Bayesian analysis Growth determinants |
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