IDEAS home Printed from https://ideas.repec.org/p/qmw/qmwecw/470.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2002/items/wp470.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Koo, Bonsoo & Seo, Myung Hwan, 2015. "Structural-break models under mis-specification: Implications for forecasting," Journal of Econometrics, Elsevier, vol. 188(1), pages 166-181.
    3. M. Hashem Pesaran & Yongcheol Shin, 2002. "Long-Run Structural Modelling," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 49-87.
    4. Fiteni, Inmaculada, 2004. "[tau]-estimators of regression models with structural change of unknown location," Journal of Econometrics, Elsevier, vol. 119(1), pages 19-44, March.
    5. Lei, J., 2013. "Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models," Other publications TiSEM d63bf400-7ff2-4a1c-8067-1, Tilburg University, School of Economics and Management.
    6. Xu, Ke-Li & Phillips, Peter C.B., 2008. "Adaptive estimation of autoregressive models with time-varying variances," Journal of Econometrics, Elsevier, vol. 142(1), pages 265-280, January.
    7. Seo, Myung Hwan & Linton, Oliver, 2007. "A smoothed least squares estimator for threshold regression models," Journal of Econometrics, Elsevier, vol. 141(2), pages 704-735, December.
    8. George Kapetanios & Andrew P. Blake, 2007. "Boosting Estimation of RBF Neural Networks for Dependent Data," Working Papers 588, Queen Mary University of London, School of Economics and Finance.
    9. Liangjun Su & Zhenlin Yang, 2008. "Asymptotics and Bootstrap for Transformed Panel Data Regressions," Development Economics Working Papers 22477, East Asian Bureau of Economic Research.
    10. Lei, J., 2013. "Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models," Discussion Paper 2013-061, Tilburg University, Center for Economic Research.
    11. Granger, Clive W J, 1995. "Modelling Nonlinear Relationships between Extended-Memory Variables," Econometrica, Econometric Society, vol. 63(2), pages 265-279, March.
    12. Ghysels, Eric & Guay, Alain, 2003. "Structural change tests for simulated method of moments," Journal of Econometrics, Elsevier, vol. 115(1), pages 91-123, July.
    13. Sadikoglu, Serhan, 2019. "Essays in econometric theory," Other publications TiSEM 99d83644-f9dc-49e3-a4e1-5, Tilburg University, School of Economics and Management.
    14. Čížek, Pavel, 2008. "General Trimmed Estimation: Robust Approach To Nonlinear And Limited Dependent Variable Models," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1500-1529, December.
    15. Michelacci, Claudio & Zaffaroni, Paolo, 2000. "(Fractional) beta convergence," Journal of Monetary Economics, Elsevier, vol. 45(1), pages 129-153, February.
    16. Hong, Seung Hyun & Phillips, Peter C. B., 2010. "Testing Linearity in Cointegrating Relations With an Application to Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 96-114.
    17. Pierre Perron & Yohei Yamamoto, 2022. "Structural change tests under heteroskedasticity: Joint estimation versus two‐steps methods," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(3), pages 389-411, May.
    18. Rebeca Jiménez-Rodríguez, 2004. "Oil Price Shocks: Testing for Non-linearity," CSEF Working Papers 115, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    19. de Lima, Pedro J. F., 1997. "On the robustness of nonlinearity tests to moment condition failure," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 251-280.
    20. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:qmw:qmwecw:470. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Nicholas Owen (email available below). General contact details of provider: https://edirc.repec.org/data/deqmwuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.