Model selection and nonnested hypothesis testing procedures are considered in three papers. The papers generalize the existing testing procedures and propose methods to improve approximations to the sampling distribution of the test statistics. The first paper proposes robust tests which...

A model selection test for non-nested misspecified diffusion models is developed by using a criterion based on the Kullback-Leibler information criterion in a new asymptotic framework that accounts for the relative significance of diffusion functions for high frequency data. The test examines...