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Please cite this article as: Magnus, J.R., Powell, O., Prüfer, P., A comparison of two model averaging techniques with an application to growth empirics. Journal of Econometrics (2009Econometrics ( ), doi:10.1016Econometrics ( /j.jeconom.2009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTA comparison of two model averaging techniques with an application to growth empirics 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. 1 A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTJEL Classification: C51, C52, C13, C11

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This article tests whether points in tennis are independent and identically distributed (iid). We model the probability of winning a point on service and show that points are neither independent nor identically distributed: winning the previous point has a positive effect on winning the current point, and at "important" points it is more dif cult for the server to win the point than at less important points. Furthermore, the weaker a player, the stronger are these effects. Deviations from iid are small, however, and hence the iid hypothesis will still provide a good approximation in many cases. The results are based on a large panel of matches played at Wimbledon 1992-1995, in total almost 90,000 points. Our panel data model takes into account the binary character of the dependent variable, uses random effects to capture the unobserved part of a player's quality, and includes dynamic explanatory variables.