Semiparametric Inference And Bandwidth Choice Under Long Memory: Experimental Evidence

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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