Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/67838
Authors: 
Xie, Tian
Year of Publication: 
2012
Series/Report no.: 
Queen's Economics Department Working Paper 1299
Abstract: 
This paper proposes a new estimator for least squares model averaging. A model average estimator is a weighted average of common estimates obtained from a set of models. We propose computing weights by minimizing a model average prediction criterion (MAPC). We prove that the MAPC estimator is asymptotically optimal in the sense of achieving the lowest possible mean squared error. For statistical inference, we derive asymptotic tests for single hypotheses and joint hypotheses on the average coefficients for the core regressors. These regressors are of primary interest to us and are included in every approximation model. To improve the finite sample performance, we also consider bootstrap tests. In simulation experiments the MAPC estimator is shown to have significant efficiency gains over existing model selection and model averaging methods. We also show that the bootstrap tests have more reasonable rejection frequency than the asymptotic tests in small samples. As an empirical illustration, we apply the MAPC estimator to cross-country economic growth models.
Subjects: 
Model Averaging
MAPC
Convex Optimization
Optimality
Statistical Inference
JEL: 
C52
C53
O40
Document Type: 
Working Paper

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