Abstract #301016

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JSM 2002 Abstract #301016
Activity Number: 176
Type: Contributed
Date/Time: Tuesday, August 13, 2002 : 8:30 AM to 10:20 AM
Sponsor: Business & Economics Statistics Section*
Abstract - #301016
Title: Estimation and Inference in Regression Models with Asymmetric Error Distributions: A Comparison of LAV and LS Procedures
Author(s): Terry Dielman*+
Affiliation(s): Texas Christian University
Address: P.O. Box 298530, Fort Worth, Texas, 76129, USA
Keywords: least squares ; least absolute value
Abstract:

A Monte Carlo simulation is used to compare estimation and inference procedures in least absolute value (LAV) and least squares (LS) regression models with asymmetric error distributions. This paper compares both bias and efficiency of coefficient estimates. Hypothesis tests for coefficients are compared on the basis of empirical level of significance and power. Several approaches to hypothesis testing for coefficients are examined for the LAV regression: likelihood ratio test, Lagrange multiplier test, and the bootstrap test. The standard t-test is used for the LS regression. Factors considered that might influence estimation and test performance include the disturbance distribution, the type of independent variable, and the sample size.

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Revised March 2002