We propose an improved model selection test for dynamic models using a new asymptotic approximation to the sampling distribution of a new test statistic. The model selection test is applicable to dynamic models with very general selection criteria and estimation methods. Since our test statistic...

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...

We provide a new asymptotic analysis of model selection procedure that compares likelihoods of two candidate diffusion models. Our asymptotic analysis relies on two dimensional asymptotic expansions with shrinking sampling interval Δ and increasing sampling span T, and clarifies the different...