期刊名称：Discussion Papers / School of Business, University of New South Wales
出版年度：2007
卷号：2007
出版社：Sydney
摘要：In time series analysis, tests for serial independence, symmetry, and goodnessof-
fit based on divergence measures, such as the Kullback-Leibler divergence or
Hellinger distance are currently receiving much interest. We consider replacing the
divergence measures in these tests by kernel-based quadratic form. In this way we
avoid the common practice of using plug-in estimators. Our approach separates the
problem of consistent estimation of the divergence measure from that of estimating
the underlying joint densities consistently. We construct a test for serial independence
on the basis of the introduced quadratic forms. An optimal bandwidth
selection is a common problem in the nonparametric econometrics. To confront
this problem we use an adaptive bandwidth procedure over a range of different
bandwidth values. In order to produce an exact test, a permutation procedure is
applied. Our results are illustrated with simulations for various data generating
processes relevant to financial econometrics. We compare the performance of our
test with existing nonparametric tests for serial independence and show that for
many processes our approach produces higher power in comparison with BDS test
and the test of Granger, Maasoumi, and Racine (2004). We apply our method to
the return series of S&P 500.
