@article{Xie:274619,
      recid = {274619},
      author = {Xie, Tian},
      title = {Least Squares Model Averaging by Prediction Criterion},
      address = {2012-11},
      number = {2110-2018-4433},
      series = {Working Paper No. 1299},
      pages = {42},
      year = {2012},
      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.},
      url = {http://ageconsearch.umn.edu/record/274619},
      doi = {https://doi.org/10.22004/ag.econ.274619},
}