The LAD estimator of the vector parameter in a linear regression is defined by minimizing the sum of the absolute values of the residuals. This paper provides a direct proof of asymptotic normality for the LAD estimator. The main theorem assumes deterministic carriers. The extension to random carriers includes the case of autoregressions whose error terms have finite second moments. For a first-order autoregression with Cauchy errors the LAD estimator is shown to converge at a 1/n rate.
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Article provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 7 (1991) Issue (Month): 02 (June) Pages: 186-199 Download reference. The following formats are available: HTML
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