Generalized Information Matrix Tests for Copulas

Abstract

We propose a family of goodness-of-fit tests for copulas. The tests use generalizations of the information matrix (IM) equality of White (1982) and so relate to the copula test proposed by Huang and Prokhorov (2014). The idea is that eigenspectrum-based statements of the IM equality reduce the degrees of freedom of the test's asymptotic distribution and lead to better size-power properties, even in high dimensions. The gains are especially pronounced for vine copulas, where additional benefits come from simplifications of score functions and the Hessian. We derive the asymptotic distribution of the generalized tests, accounting for the non-parametric estimation of the marginals and apply a parametric bootstrap procedure, valid when asymptotic critical values are inaccurate. In Monte Carlo simulations, we study the behavior of the new tests, compare them with several Cramer-von Mises type tests and confirm the desired properties of the new tests in high dimensions.