Using generalized functions of random variables and generalized Taylor series expansions, we provide quick 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, 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.
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Article provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 7 (1991) Issue (Month): 04 (December) Pages: 450-463 Download reference. The following formats are available: HTML
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