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Lack of fit test for long memory regression models

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  • Lihong Wang

    (Nanjing University)

Abstract

This paper proposes a test for assessing the accuracy of an assumed nonlinear regression model with long memory design and heteroscedastic long memory errors. The test is based on the marked empirical process. The asymptotic distributions of the proposed test statistics are investigated. The leave-one-observation-out kernel type estimator of the conditional variance function is also constructed in order to implement the lack of fit test.

Suggested Citation

  • Lihong Wang, 2020. "Lack of fit test for long memory regression models," Statistical Papers, Springer, vol. 61(3), pages 1043-1067, June.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:3:d:10.1007_s00362-017-0974-9
    DOI: 10.1007/s00362-017-0974-9
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    References listed on IDEAS

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