Rank-based testing for semiparametric VAR models: A measure transportation approach
Marc Hallin, Davide La Vecchia, Hang Liu
Abstract
We develop a class of tests for semiparametric vector autoregressive (VAR) models with unspecified innovation densities based on the recent measure-transportation-based concepts of multivariate center-outward ranks and signs. We show that these concepts, combined with Le Cam’s asymptotic theory of statistical experiments, yield novel testing procedures, which (a) are valid under a broad class of innovation densities (possibly non-elliptical, skewed, and/or with infinite moments), (b) are optimal (locally asymptotically maximin or most stringent) at selected ones, and (c) are robust against additive outliers. In order to show this, we establish, for a general class of center-outward rank-based serial statistics, a Hájek asymptotic representation result, of independent interest, which allows for a rank-based reconstruction of central sequences. As an illustration, we consider the problems of testing the absence of serial correlation in multiple-output and possibly non-linear regression (an extension of the classical Durbin-Watson problem) and the sequential identification of the order p of a VAR(p) model. A Monte Carlo comparative study of our tests and their routinely-applied Gaussian competitors demonstrates the benefits (in terms of size, power, and robustness) of our methodology; these benefits are particularly significant in the presence of asymmetric and leptokurtic innovation densities. A real-data application concludes the paper.