Wednesday, September 13, 2017 at 11:40am to 1:10pm
Uris Hall, 498
University of Wisconsin, Madison
This paper proposes a new model selection test for the statistical comparison of semi/non parametric models based on a general quasi-likelihood ratio criterion. An important feature of the new test is its uniformly exact asymptotic size in the overlapping nonnested case, as well as in the easier nested and strictly nonnested cases. The uniform size control is achieved without using pre-testing, sample-splitting, or simulated critical values. We also show that the test has nontrivial power against all √n-local alternatives and against some local alternatives that converge to the null faster than √n. Finally, we provide a framework for conducting uniformly valid post model selection inference for model parameters. The finite sample performance of the uniform test and that of the post model selection inference procedure are illustrated in a mean-regression example by Monte Carlo.