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Testing for Neglected Nonlinearity in Long Memory Models

Author

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  • Richard T. Baillie

    (Queen Mary, University of London)

  • George Kapetanios

Abstract

This paper constructs tests for the presence of nonlinearity of unknown form in addition to a fractionally integrated, long memory component in a time series process. The tests are based on artificial neural network structures and do not restrict the parametric form of the nonlinearity. The tests only require a consistent estimate of the long memory parameter. Some theoretical results for the new tests are obtained and detailed simulation evidence is also presented on the power of the tests. The new methodology is then applied to a wide variety of economic and financial time series.

Suggested Citation

  • Richard T. Baillie & George Kapetanios, 2005. "Testing for Neglected Nonlinearity in Long Memory Models," Working Papers 528, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:528
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    References listed on IDEAS

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    More about this item

    Keywords

    Long memory; Non-linearity; Artificial neural networks; Realized volatility; Absolute returns; Real exchange rates; Unemployment;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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