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An Artificial Neural Network Test For Structural Change With Unspecified Parametric Form

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  • Yoshihisa Suzuki

Abstract

Tests for a structural change of unknown timing in parameterized regression functions have been introduced previously under the maintained assumption that the models are correctly specified. However, the existing family of tests are unable to discriminate between structural change and misspecification. This paper introduces test statistics which do not require specification of the parametric form of the underlying data‐generating process (DGP). I approximate it by a version of artificial neural networks (ANN). My simulation studies indicate that an ANN approximates the DGP quite well and that the derived tests have good power relative to the power envelope. JEL Classification Numbers: C12, C14, C45.

Suggested Citation

  • Yoshihisa Suzuki, 2001. "An Artificial Neural Network Test For Structural Change With Unspecified Parametric Form," The Japanese Economic Review, Japanese Economic Association, vol. 52(3), pages 339-365, September.
    • Handle: RePEc:bla:jecrev:v:52:y:2001:i:3:p:339-365
    DOI: 10.1111/1468-5876.00199
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    Cited by:

    1. Wei Liu & Yoshihisa Suzuki & Shuyi Du, 2024. "Forecasting the Stock Price of Listed Innovative SMEs Using Machine Learning Methods Based on Bayesian optimization: Evidence from China," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 2035-2068, May.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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