A Shortcut to LAD Estimator Asymptotics

Peter Phillips

No 949, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: Using generalized functions of random variables and generalized Taylor series expansions, we provide almost trivial demonstrations of the asymptotic theory for the LAD estimator in a regression model setting. The approach is justified by the smoothing that is delivered in the limit by the asymptotics, whereby the generalized functions are forced to appear as linear functionals wherein they become real valued. Models with fixed and random regressors, autoregressions and autoregressions with infinite variance errors are studied. Some new analytic results are obtained including an asymptotic expansion of the distribution of the LAD estimator and the results of some earlier simulation studies are examined.

Keywords: Autoregression; delta sequence; density estimate; domain of attraction; generalized function; generalized Taylor series; LAD estimator; stable process (search for similar items in EconPapers)
Pages: 20 pages
Date: 1990-07
Note: CFP 801.
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Published in Econometric Theory (1991), 7: 450-463

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Journal Article: A Shortcut to LAD Estimator Asymptotics (1991)
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