Applications of robust estimation techniques in demand analysis

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Abstract
This paper addresses the problems associated with applying robust estimaion techniques to demand analysis. Three questions are considered: (1) How well do alternative robust techniques perform in comparison with traditional least-squares techniques? In nearly all cases these estimators outperform the least-squares estimator. (2) How fragile is the distributional assumption of normality in demand analysis? Our study presents evidence which indicates that a proper demand equation specification should in certain cases include both fat- and thin-tailed alternatives to the normal distribution. The degree of quantitative sensitivity in results is on an even keel with traditional movements caused by changes in functional form or explanatory variables. (3) What is the relationship between assumptions about the distribution of the error process and resultant elasticity measures? Elasticity estimates derived from our demand equations are found to change by orders of magnitude when distributional assumption in a neighbourhood of a normal distribution are applied to a fixed structural model.

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