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Title: Model averaging for two non-nested models Authors:  Yan Gao - Minzu University of China (China) [presenting]
Abstract: The Mallows model averaging approach is proposed to be used for two non-nested models. It is proved that the obtained weight of true model converges to 1 with root-n rate. It develops a penalized Mallows criterion which ensures that the weight of the true model equals 1 with probability tending to 1. Simulation results indicate the consistency and also show the model averaging approach performs better than the estimation post J-test.