Data-driven smooth tests for the martingale difference hypothesis
Juan Carlos Escanciano () and
Silvia Mayoral
Computational Statistics & Data Analysis, 2010, vol. 54, issue 8, 1983-1998
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 smooth tests are shown to be optimal in a semiparametric sense discussed in the paper, and they are robust to conditional heteroscedasticity of unknown form. A simulation study shows that the data-driven smooth tests perform very well for a wide range of realistic alternatives and have more power than 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. The paper also contains a new weak convergence theorem that is of independent interest.
Keywords: Nonlinear; time; series; Empirical; processes; Pivotal; tests; Neyman's; tests; Semiparametric; efficiency; Market; efficiency (search for similar items in EconPapers)
Date: 2010
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Related works:
Working Paper: Data-Driven Smooth Tests for the Martingale Difference Hypothesis (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:54:y:2010:i:8:p:1983-1998
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