Asymptotics of M-estimators in non-linear regression with long memory designs

Hira L. Koul and Richard T. Baillie

Statistics & Probability Letters, 2003, vol. 61, issue 3, 237-252

Abstract: This paper derives the asymptotic distribution of a class of M-estimators in a family of non-linear regression models when the errors and the design variables are long memory moving averages. The class of estimators includes analogs of the least square, least absolute deviation and the Huber(c) estimators. A simulation study comparing the finite sample behaviour of the least absolute deviation and the least-square estimators is also included.

Keywords: Least; absolute; deviation; estimators; Root; mean; squared; error; Forward; premiums (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (1)

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