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DETECTION OF NONCONSTANT LONG MEMORY PARAMETER

Published online by Cambridge University Press:  16 October 2013

Frédéric Lavancier ,
Remigijus Leipus ,
Anne Philippe  and
Donatas Surgailis
Frédéric Lavancier
Affiliation:
Université de Nantes
Remigijus Leipus*
Affiliation:
Vilnius University
Anne Philippe
Affiliation:
Université de Nantes
Donatas Surgailis
Affiliation:
Vilnius University
*
*Address correspondence to Remigijus Leipus, Vilnius University, Lithuania; e-mail: remigijus.leipus@mif.vu.it.
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Abstract

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This article deals with detection of a nonconstant long memory parameter in time series. The null hypothesis presumes stationary or nonstationary time series with a constant long memory parameter, typically an I (d) series with d > −.5 . The alternative corresponds to an increase in persistence and includes in particular an abrupt or gradual change from I (d1) to I (d2), −.5 < d1 < d2. We discuss several test statistics based on the ratio of forward and backward sample variances of the partial sums. The consistency of the tests is proved under a very general setting. We also study the behavior of these test statistics for some models with a changing memory parameter. A simulation study shows that our testing procedures have good finite sample properties and turn out to be more powerful than the KPSS-based tests (see Kwiatkowski, Phillips, Schmidt and Shin, 1992) considered in some previous works.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 29 , Issue 5 , October 2013 , pp. 1009 - 1056
Copyright
Copyright © Cambridge University Press 2013 

Footnotes

The second and fourth authors are supported by a grant (No. MIP-11155) from the Research Council of Lithuania. We are grateful to three anonymous referees for their insightful comments that greatly improved the manuscript.

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