Data-Driven Smooth Tests for the Martingale Difference Hypothesis

Juan Carlos Escanciano and Silvia Mayoral

No 01/07, Faculty Working Papers from School of Economics and Business Administration, University of Navarra

Abstract: A general method for testing the martingale difference hypothesis is proposed. The new tests are data-driven smooth tests based on the principal components of certain marked empirical processes that are asymptotically distribution-free, with critical values that are already tabulated. The data-driven smooth tests are optimal in a semiparametric sense discussed in the paper, and they are robust to conditional heteroskedasticity of unknown form. A simulation study shows that the smooth tests perform very well for a wide range of realistic alternatives and have more power than the omnibus and other competing tests. Finally, an application to the S&P 500 stock index and some of its components highlights the merits of our approach.

Pages: 30 pages
Date: 2007-01-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (2)

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Journal Article: Data-driven smooth tests for the martingale difference hypothesis (2010)
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