Bootstrapping long memory tests: Some Monte Carlo results

Authors:
A. Murphy;M. Izzeldin
Affiliations:
Hertford College, University of Oxford, Oxford OX1 3BW, UK;Lancaster University Management School, Lancaster, UK
Venue:
Computational Statistics & Data Analysis
Year:
2009

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Abstract

The bootstrapped size and power properties of six long memory tests-the modified R/S, KPSS, V/S, GPH, Robinson's H@^ and the recently proposed S@^"k tests-are investigated. Even in samples of size 100, the moving block bootstrap controls the empirical size of the tests in the DGPs examined. The H@^ test appears to be the most powerful. Moreover, compared with asymptotic tests, the bootstrap tests suffer little loss of power against fractionally integrated processes in samples with 250 or more observations. This is true both for distributions with heavy tails and with stochastic volatility.