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




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:wp470

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    References listed on IDEAS

    1. Machin, Stephen & Alan Manning & Lupin Rahman, 2003. "Where Minimum Wage Bites Hard: The Introduction of the UK National Minimum Wage to a Low Wage Sector," Royal Economic Society Annual Conference 2003 145, Royal Economic Society.
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    6. Machin, Stephen & Manning, Alan & Rahman, Lupin, 2002. "Where the minimum wage bites hard: the introduction of the UK national minimum wage to a low wage sector," LSE Research Online Documents on Economics 20070, London School of Economics and Political Science, LSE Library.
    7. Stephen Machin & Alan Manning, 1992. "Minimum Wages," CEP Discussion Papers dp0080, Centre for Economic Performance, LSE.
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    Cited by:

    1. Kefei You & Nicholas Sarantis, 2013. "Structural breaks, rural transformation and total factor productivity growth in China," Journal of Productivity Analysis, Springer, vol. 39(3), pages 231-242, June.

    More about this item


    Nonlinearity; Structural breaks; Neural networks;

    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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