Generalized Method of Moments with Tail Trimming

نویسندگان

  • Jonathan B. Hill
  • Eric Renault
چکیده

We develop a GMM estimator for stationary heavy tailed data by trimming an asymptotically vanishing sample portion of the estimating equations. Trimming ensures the estimator is asymptotically normal, and self-normalization implies we do not need to know the rate of convergence. Tail -trimming, however, ensures asymmetric models are covered under rudimentary assumptions about the thresholds, and it implies possibly heterogeneous convergence rates below, at or above √ T . Further, it implies super√ T -consistency is achievable depending on regressor and error tail thickness and feedback, with a rate equivalent to the largest possible rate amongst untrimmed minimum distance estimators for linear models with iid errors, and a faster rate than QML for heavy tailed GARCH. In the latter cases the optimal rate is achieved with the efficient GMM weight, and by using simple rules of thumb for choosing the number of trimmed equations. Simulation evidence shows the new estimator dominates GMM and QML when these estimators are not or have not been shown to be asymptotically normal.

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تاریخ انتشار 2010