Various structural and non-structural models of productivity growth have been proposed in the literature. In either class of models, predictive measurements of productivity and efficiency are obtained. This paper examines the model averaging approaches of Hansen and Racine (2012), which can provide a vehicle to weight predictions (in the form of productivity and efficiency measurements) from different non-structural methods. We first describe the jackknife model averaging estimator proposed by Hansen and Racine (2012) and illustrate how to apply the technique to a set of competing stochastic frontier estimators. The derived method is then used to analyze productivity and efficiency dynamics in 25 highly-industrialized countries over the period 1990 to 2014. Through the empirical application, we show that the model averaging method provides relatively stable estimates, in comparison to standard model selection methods that simply select one model with the highest measure of goodness of fit.
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