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On change point detection in regression function using nonparametric autoregressive processes

Author

Listed:
  • Ben Célestin Kouassi

    (Institut National Polytechnique Félix Houphouët-Boigny)

  • Ouagnina Hili

    (Institut National Polytechnique Félix Houphouët-Boigny)

  • Edoh Katchekpele

    (Université de Kara)

Abstract

We propose a new test to detect a change in the conditional mean of Nonparametric AutoRegressive processes. This test is based on the combining CUSUM and marked empirical processes. Our CUSUM test requires an estimator of the regression function to make it asymptotically distribution free under the no change null hypothesis. As a consequence, we obtain convergence of the developed test statistic. We show the asymptotic consistency of the test when the difference between the regression functions before and after the break is constant. We illustrate the applicability of our method by the means of Monte–Carlo simulation.

Suggested Citation

  • Ben Célestin Kouassi & Ouagnina Hili & Edoh Katchekpele, 2025. "On change point detection in regression function using nonparametric autoregressive processes," Indian Journal of Pure and Applied Mathematics, Springer, vol. 56(1), pages 191-209, March.
    • Handle: RePEc:spr:indpam:v:56:y:2025:i:1:d:10.1007_s13226-023-00471-6
    DOI: 10.1007/s13226-023-00471-6
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    References listed on IDEAS

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    3. Hong, Seok Young & Linton, Oliver, 2020. "Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff," Journal of Econometrics, Elsevier, vol. 219(2), pages 389-424.
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