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Asymptotics for Least Absolute DeviationRegression Estimators

Published online by Cambridge University Press:  11 February 2009

David Pollard
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Abstract

The LAD estimator of the vector parameter in a linearregression is defined by minimizing the sum of theabsolute values of the residuals. This paperprovides a direct proof of asymptotic normality forthe LAD estimator. The main theorem assumesdeterministic carriers. The extension to randomcarriers includes the case of autoregressions whoseerror terms have finite second moments. For afirst-order autoregression with Cauchy errors theLAD estimator is shown to converge at a1/n rate.

Information

Type
Research Article
Information
Econometric Theory , Volume 7 , Issue 2 , June 1991 , pp. 186 - 199
Copyright
Copyright © Cambridge University Press 1991

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