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Testing for Structural Breaks in Nonlinear Dynamic Models Using Artificial Neural Network Approximations

Author

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  • George Kapetanios

    (Queen Mary, University of London)

Abstract

In this paper we suggest a number of statistical tests based on neural network models, that are designed to be powerful against structural breaks in otherwise stationary time series processes while allowing for a variety of nonlinear specifications for the dynamic model underlying them. It is clear that in the presence of nonlinearity standard tests of structural breaks for linear models may not have the expected performance under the null hypothesis of no breaks because the model is misspecified. We therefore proceed by approximating the conditional expectation of the dependent variable through a neural network. Then, the residual from this approximation is tested using standard residual based structural break tests. We investigate the asymptoptic behaviour of residual based structural break tests in nonlinear regression models. Monte Carlo evidence suggests that the new tests are powerful against a variety of structural breaks while allowing for stationary nonlinearities.

Suggested Citation

  • George Kapetanios, 2002. "Testing for Structural Breaks in Nonlinear Dynamic Models Using Artificial Neural Network Approximations," Working Papers 470, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:470
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    References listed on IDEAS

    as
    1. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037, Decembrie.
    2. Andrews, Donald W K, 1987. "Consistency in Nonlinear Econometric Models: A Generic Uniform Law of Large Numbers [On Unification of the Asymptotic Theory of Nonlinear Econometric Models]," Econometrica, Econometric Society, vol. 55(6), pages 1465-1471, November.
    3. Lee, Tae-Hwy & White, Halbert & Granger, Clive W. J., 1993. "Testing for neglected nonlinearity in time series models : A comparison of neural network methods and alternative tests," Journal of Econometrics, Elsevier, vol. 56(3), pages 269-290, April.
    4. Delgado, Miguel A. & Hidalgo, Javier, 2000. "Nonparametric inference on structural breaks," Journal of Econometrics, Elsevier, vol. 96(1), pages 113-144, May.
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    Cited by:

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    2. J. Hoyo & G. Llorente & C. Rivero, 2019. "Testing for Constant Parameters in Nonlinear Models: A Quick Procedure with an Empirical Illustration," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 113-137, June.
    3. Onyango, Christopher H., 2010. "Liberalization of Services and its Implications on Cross-Border Agricultural Trade in Eastern Africa," Conference papers 332028, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.

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    More about this item

    Keywords

    Nonlinearity; Structural breaks; Neural networks;
    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
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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