Semiparametric Inference And Bandwidth Choice Under Long Memory: Experimental Evidence

Year 2013, Volume: 6 Issue: 1, 27 - 41, 01.01.2013

Uwe Hassler Maya Olivares

Abstract

The most widely used semiparametric estimators under fractional integration are variants of the local Whittle [LW] estimator. They are consistent for the long memory parameter d and follow a limiting normal distribution. Such properties require the bandwidth m to satisfy certain restrictions for the estimators to be “local” or semiparametric in large samples. Optimal rates for m are known and data-driven selection procedures have been proposed. A Monte Carlo study is conducted to compare the performance of the LW and the so-called exact LW estimators both in terms of experimental size when testing hypotheses about d and in terms of root mean squared error. In particular, the choice of the bandwidth is addressed. Further, competing approximations to limiting normality are compared.

Keywords

Fractional integration, approximate normality, bandwidth selection

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