Lack of fit test for long memory regression models

Lihong Wang ()
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Lihong Wang: Nanjing University

Statistical Papers, 2020, vol. 61, issue 3, No 6, 1043-1067

Abstract: 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.

Keywords: Kernel estimation; Lack of fit test; Marked empirical process; Long memory; 62M10; 62G10; 62G20 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00362-017-0974-9

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