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Comparison of Bootstrap Methods for LAD Estimator in AR(1) Model

  • Kang, Kee-Hoon (Department of Statistics, Hankuk University of Foreign Studies) ;
  • Shin, Key-Il (Department of Statistics, Hankuk University of Foreig Studies)
  • Published : 2006.12.31

Abstract

It has been shown that LAD estimates are more efficient than LS estimates when the error distribution is double exponential in AR(1) model. In order to explore the performance of LAD estimates one can use bootstrap approaches. In this paper we consider the efficiencies of bootstrap methods when we apply LAD estimates with highly variable data. Monte Carlo simulation results are given for comparing generalized bootstrap, stationary bootstrap and threshold bootstrap methods.

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