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Testing for Neglected Nonlinearity in Long Memory Models

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Author Info
Richard T. Baillie () (Queen Mary, University of London)
George Kapetanios () (Queen Mary, University of London)

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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.

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File URL: http://www.econ.qmul.ac.uk/papers/doc/wp528.pdf
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Publisher Info
Paper provided by Queen Mary, University of London, Department of Economics in its series Working Papers with number 528.

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Date of creation: Apr 2005
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Handle: RePEc:qmw:qmwecw:wp528

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Related research
Keywords: Long memory Non-linearity Artificial neural networks Realized volatility Absolute returns Real exchange rates Unemployment

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
F31 - International Economics - - International Finance - - - Foreign Exchange

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This page was last updated on 2008-10-30.


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