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Testing multivariate hypotheses with positive definite bilinear forms

Listed author(s):
  • Valentyn Panchenko
  • Cees Diks

In time series analysis, tests for independence, symmetry, and goodness-of-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 positive definite bilinear forms. By doing so, we can use U-statistics estimators for the functional of interest (the divergence between two distributions). In this way we avoid the common practice of using plug-in estimators. In addition, our approach separates the problem of consistent estimation of the divergence measure from that of estimating the underlying joint densities consistently. The approach demonstrates why in a testing context, the bandwidth may tend to zero with the sample size slower than required for consistent density estimation. Our results are illustrated with simulations

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 201.

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Date of creation: 11 Aug 2004
Handle: RePEc:sce:scecf4:201
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