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Adjusted blockwise empirical likelihood for long memory time series models

Feifan Jiang and Lihong Wang ()
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Feifan Jiang: Nanjing University
Lihong Wang: Nanjing University

Statistical Methods & Applications, 2018, vol. 27, issue 2, 319-332

Abstract: Abstract In this paper, we introduce an adjusted blockwise empirical likelihood (ABEL) method for long memory time series models. By dividing time series into blocks and by adding an appropriate adjustment term, we construct the ABEL ratio and the confidence interval for the mean of the process. Under mild conditions, we show that Wilks’ theorem still holds for the ABEL ratio by choosing a specific block correction factor. The Monte Carlo simulation studies are reported to assess the finite sample performance of the proposed ABEL method.

Keywords: Adjusted blockwise empirical likelihood; Confidence interval; Long memory time series models; Wilks’ theorem; 62M10 (search for similar items in EconPapers)
Date: 2018
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