Valid Edgeworth Expansion of the Bootstrap t-statistic of the Whittle MLE for Linear Regression Models with Long-Memory Residuals

Mosisa Aga ()
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Mosisa Aga: Auburn University at Montgomery

Sankhya A: The Indian Journal of Statistics, 2024, vol. 86, issue 2, No 9, 920-950

Abstract: Abstract In this paper we provide a valid Edgeworth expansion of the parametric bootstrap t-statistic for the Whittle maximum likelihood estimator of a linear regression time series model whose residuals are stationary, Gaussian, and long-memory. Under some sets of conditions on the spectral density function and the parametric values, an Edgeworth expansion of the bootstrap t-statistic of arbitrarily large order of the model is established to have an error of $$o(n^{1-s/2})$$ o ( n 1 - s / 2 ) , where $$s \ge 3$$ s ≥ 3 is a positive integer. The result is obtained by extending the Edgeworth expansion obtained by Andrew et al. (2006), which was established for the parametric bootstrap t-statistic of the same model without the linear regression component.

Keywords: Edgeworth expansion; Cumulant; Parametric bootstrap; t-statistic; Whittle maximum likelihood estimator; Linear regression; Long memory; 62M10 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13171-024-00361-x

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