Volume 14 - Issue 3 (Forthcoming) (1) | PP: 47 - 55
Language : English
DOI : https://doi.org/10.31559/glm2024.14.3.1
DOI : https://doi.org/10.31559/glm2024.14.3.1
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Bootstrap Procedure for Correlation Model of Random Design Under Strong Dependence
Received Date | Revised Date | Accepted Date | Publication Date |
14/6/2024 | 4/7/2024 | 11/7/2024 | 28/7/2024 |
Abstract
This paper investigates the validity of a bootstrap least square estimate of a polynomial correlation model whose error terms are an autoregressive fractionally integrated moving average ARFIMA (p,d,q) strongly dependent time series. For an (r + 1)*1 vector B of unknown parameters, ^Ba an 'adjusted' least square estimate of B, ^B* a bootstrap estimate of B, it is shown that the distribution of ✓n(^ B*- ^Ba) converges to that of ✓n (^Ba - B), where n is the sample size. The result in this paper extends the correlation part of the results obtained by [9] and [6] to the case where the error term exhibits a long memory time series.
How To Cite This Article
Aga , M. (2024). Bootstrap Procedure for Correlation Model of Random Design Under Strong Dependence . General Letters in Mathematics, 14 (3), 47-55, 10.31559/glm2024.14.3.1
Copyright © 2024, This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.