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On a family of test statistics for discretely observed diffusion processes
Alessandro De Gregorio, University of Rome, La Sapienza
Stefano Iacus, Department of Economics, Business and Statistics, University of Milan, IT
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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.
SUGGESTED CITATION:
Alessandro De Gregorio and Stefano Iacus,
"On a family of test statistics for discretely observed diffusion processes"
(November 2011).
UNIMI - Research Papers in Economics, Business, and Statistics.
Statistics and Mathematics.
Working Paper 53.
http://services.bepress.com/unimi/statistics/art53
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