Abstract for Research Paper 2004-16

Apart from examining for autocorrelation and heteroscedasticity, applied econometricians seldom give much attention to the properties of the stochastic terms of their regression models. Most of the time, least-squares estimation (in some form or another) is employed, based upon a belief (often no more than implicit) that the conditions needed for the validity of the Gauss-Markov and Classical Normal Central-Limit Theorems are ever present. In fact, there are a lot of reasons as to why real-world error terms may not behave in ways that these conditions require. Residuals from Engel curves and demand functions estimated from data from the BLS quarterly consumer expenditure surveys provides a rather striking instance of this, in that the distribution of these residuals are almost invariably asymmetrical and fat-tailed. The focus of the present exercise is on the use of quantile regression as a robust corrective.


	Quantile Regression Analysis of Asymmetrically Distributed Residuals in Consumer Demand Equations Lester D. Taylor
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	Abstract
	Apart from examining for autocorrelation and heteroscedasticity, applied econometricians seldom give much attention to the properties of the stochastic terms of their regression models. Most of the time, least-squares estimation (in some form or another) is employed, based upon a belief (often no more than implicit) that the conditions needed for the validity of the Gauss-Markov and Classical Normal Central-Limit Theorems are ever present. In fact, there are a lot of reasons as to why real-world error terms may not behave in ways that these conditions require. Residuals from Engel curves and demand functions estimated from data from the BLS quarterly consumer expenditure surveys provides a rather striking instance of this, in that the distribution of these residuals are almost invariably asymmetrical and fat-tailed. The focus of the present exercise is on the use of quantile regression as a robust corrective.