On a family of test statistics for discretely observed diffusion processes

TL;DR

This paper introduces a family of test statistics based on $\,phi$-divergence measures for hypothesis testing in discretely observed ergodic diffusion processes, demonstrating their asymptotic properties and finite-sample performance.

Contribution

It develops a new family of asymptotically distribution-free test statistics for diffusion processes, analyzing their theoretical properties and finite-sample behavior.

Findings

Tests are asymptotically chi-squared distributed.

Performance depends on the choice of $\,phi$ function in small samples.

No uniformly most powerful test within the family.

Abstract

We consider parametric hypotheses testing for multidimensional ergodic diffusion processes observed at discrete time. We propose a family of test statistics, related to the so called $\phi$-divergence measures. By taking into account the quasi-likelihood approach developed for studying the stochastic differential equations, it is proved that the tests in this family are all asymptotically distribution free. In other words, our test statistics weakly converge to the chi squared distribution. Furthermore, our test statistic is compared with the quasi likelihood ratio test. In the case of contiguous alternatives, it is also possible to study in detail the power function of the tests. Although all the tests in this family are asymptotically equivalent, we show by Monte Carlo analysis that, in the small sample case, the performance of the test strictly depends on the choice of the function $\phi$. Furthermore, in this framework, the simulations show that there are not uniformly most powerful tests.