A New Bispectral Test for Nonlinear Serial Dependence
AbstractNonconstancy of the bispectrum of a time series has been taken as a measure of non-Gaussianity and nonlinear serial dependence in a stochastic process by Subba Rao and Gabr (1980) and by Hinich (1982), leading to Hinich's statistical test of the null hypothesis of a linear generating mechanism for a time series. Hinich's test has the advantage of focusing directly on nonlinear serial dependenceâin contrast to subsequent approaches, which actually test for serial dependence of any kind (nonlinear or linear) on data which have been pre-whitened. The Hinich test tends to have low power, however, and (in common with most statistical procedures in the frequency domain) requires the specification of a smoothing or window-width parameter. In this article, we develop a modification of the Hinich bispectral test which substantially ameliorates both of these problems by the simple expedient of maximizing the test statistic over the feasible values of the smoothing parameter. Monte Carlo simulation results are presented indicating that the new test is well sized and has substantially larger power than the original Hinich test against a number of relevant alternatives; the simulations also indicate that the new test preserves the Hinich test's robustness to misspecifications in the identification of a pre-whitening model.
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Bibliographic InfoPaper provided by Virginia Polytechnic Institute and State University, Department of Economics in its series Working Papers with number e06-6.
Length: 18 pages
Date of creation: 2006
Date of revision:
Bispectrum; nonlinearity; time series analysis;
Other versions of this item:
- Elena Rusticelli & Richard Ashley & Estela Bee Dagum & Douglas Patterson, 2009. "A New Bispectral Test for NonLinear Serial Dependence," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 279-293.
- NEP-ALL-2006-11-18 (All new papers)
- NEP-ECM-2006-11-18 (Econometrics)
- NEP-ETS-2006-11-18 (Econometric Time Series)
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