Bootstrap Methods for Median Regression Models

Preprint

preprint

Published in RePEc

Abstract

The least-absolute-deviations (LAD) estimator for a median-regression model does not satisfy the standard conditions for obtaining asymptotic refinements through use of the bootstrap because the LAD objective function is not smooth. This paper overcomes this problem by smoothing the objective function so that it becomes differentiable. The smoothed estimator is asymptotically equivalent to the standard LAD estimator. With bootstrap critical values, the levels of symmetrical t and c2 tests based on the smoothed estimator are correct through O(n-g), where g

Keywords

MEDIAN REGRESSION MODEL
ABSOLUTE DEVIATIONS
SMOOTHED ESTIMATOR
BOOTSTRAP METHODS
CRITICAL VALUES
STANDARD
LAD

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