Whittle-type estimation under long memory and nonstationarity

(with Uwe HASSLER)

Published in AStA Advances in Statistical Analysis, 2019

Cheung, Y.L. & U. Hassler (2020). "Whittle-type estimation under long memory and nonstationarity." AStA Advances in Statistical Analysis, 104, 363-383. doi: 10.1007/s10182-019-00358-0

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We consider six variants of (local) Whittle estimators of the fractional order of integration d. They follow a limiting normal distribution under stationarity as well as under (a certain degree of) nonstationarity. Experimentally, we observe a lack of continuity of the objective functions of the two fully extended versions at d=1/2 that has not been reported before. It results in a pileup of the estimates at d=1/2 when the true value is in a neighborhood to this half point. Consequently, studentized test statistics may be heavily oversized. The other four versions suffer from size distortions, too, although of a different pattern and to a different extent.

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