Nonparametric Tests for Serial Independence Based on Quadratic Forms
AbstractTests for serial independence and goodness-of-fit based on divergence notions between probability distributions, such as the Kullback-Leibler divergence or Hellinger distance, have recently received much interest in time series analysis. The aim of this paper is to introduce tests for serial independence using kernel-based quadratic forms. This separates the problem of consistently estimating the divergence measure from that of consistently estimating the underlying joint densities, the existence of which is no longer required. Exact level tests are obtained by implementing a Monte Carlo procedure using permutations of the original observations. The bandwidth selection problem is addressed by introducing a multiple bandwidth procedure based on a range of different bandwidth values. After numerically establishing that the tests perform well compared to existing nonparametric tests, applications to estimated time series residuals are considered. The approach is illustrated with an application to financial returns data.
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Bibliographic InfoPaper provided by Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance in its series CeNDEF Working Papers with number 05-13.
Date of creation: 2005
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- Juan Mora & Miguel A. Delgado, 1999.
"- A Nonparametric Test For Serial Independence Of Regression Errors,"
Working Papers. Serie AD
1999-28, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
- Delgado, Miguel A. & Mora, Juan, . "A Nonparametric Test for Serial Independence of Regression Errors," Open Access publications from Universidad Carlos III de Madrid info:hdl:10016/2447, Universidad Carlos III de Madrid.
- Rosenblatt, Murray & Wahlen, Bruce E., 1992. "A nonparametric measure of independence under a hypothesis of independent components," Statistics & Probability Letters, Elsevier, vol. 15(3), pages 245-252, October.
- Ahmad, Ibrahim A. & Li, Qi, 1997. "Testing independence by nonparametric kernel method," Statistics & Probability Letters, Elsevier, vol. 34(2), pages 201-210, June.
- Heer, Georg R., 1991. "Testing independence in high dimensions," Statistics & Probability Letters, Elsevier, vol. 12(1), pages 73-81, July.
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