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A nonparametric test for weak dependence against strong cycles and its bootstrap analogue

Hidalgo, Javier (2007) A nonparametric test for weak dependence against strong cycles and its bootstrap analogue. Journal of Time Series Analysis, 28 (3). pp. 307-349. ISSN 0143-9782

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Identification Number: 10.1111/j.1467-9892.2006.00510.x


We examine a test for the hypothesis of weak dependence against strong cyclical components. We show that the limiting distribution of the test is a Gumbel distribution, denoted G(·). However, since G(·) may be a poor approximation to the finite sample distribution, being the rate of the convergence logarithmic [see Hall Journal of Applied Probability (1979), Vol. 16, pp. 433–439], inferences based on G(·) may not be very reliable for moderate sample sizes. On the other hand, in a related context, Hall [Probability Theory and Related Fields (1991), Vol. 89, pp. 447–455] showed that the level of accuracy of the bootstrap is significantly better. For that reason, we describe an approach to bootstrapping the test based on Efron's [Annals of Statistics (1979), Vol. 7, pp. 1–26] resampling scheme of the data. We show that the bootstrap principle is consistent under very mild regularity conditions.

Item Type: Article
Official URL:
Additional Information: © 2007 John Wiley & Sons, Inc
Divisions: Economics
Subjects: H Social Sciences > HB Economic Theory
Q Science > QA Mathematics
JEL classification: C - Mathematical and Quantitative Methods > C0 - General
Date Deposited: 20 Apr 2011 07:54
Last Modified: 20 Mar 2021 02:44

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