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Tests Of The Martingale Difference Hypothesis Using Boosting And Rbf Neural Network Approximations

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  • Kapetanios, George
  • Blake, Andrew P.

Abstract

The martingale difference restriction is an outcome of many theoretical analyses in economics and finance. A large body of econometric literature deals with tests of that restriction. We provide new tests based on radial basis function (RBF) neural networks. Our work is based on the test design of Blake and Kapetanios (2000, 2003a, 2003b). However, unlike that work we provide a formal theoretical justification for the validity of these tests and present some new general theoretical results. These results take advantage of the link between the algorithms of Blake and Kapetanios (2000, 2003a, 2003b) and boosting. We carry out a Monte Carlo study of the properties of the new tests and find that they have very good power performance. A simplified implementation of boosting is found to have desirable properties and small computational cost. An empirical application to the S&P 500 constituents illustrates the usefulness of our new test.

Suggested Citation

  • Kapetanios, George & Blake, Andrew P., 2010. "Tests Of The Martingale Difference Hypothesis Using Boosting And Rbf Neural Network Approximations," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1363-1397, October.
    • Handle: RePEc:cup:etheor:v:26:y:2010:i:05:p:1363-1397_99
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    Cited by:

    1. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    2. Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios & James Mitchell & Aristeidis Raftapostolos, 2023. "Deep Neural Network Estimation in Panel Data Models," Working Papers 23-15, Federal Reserve Bank of Cleveland.
    3. Weiwei Liu & Zhile Yang & Kexin Bi, 2017. "Forecasting the Acquisition of University Spin-Outs: An RBF Neural Network Approach," Complexity, Hindawi, vol. 2017, pages 1-8, October.
    4. Lee Jinu, 2019. "A Neural Network Method for Nonlinear Time Series Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-18, January.

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