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Characterizing the Distribution of Macronutrient Intake among U.S. Adults: A Quantile Regression Approach
Author(s) -
Variyam Jayachandran N.,
Blaylock James,
Smallwood David
Publication year - 2002
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.1111/1467-8276.00310
Subject(s) - quantile regression , statistics , quantile , ordinary least squares , econometrics , regression , mathematics , regression analysis , conditional probability distribution , distribution (mathematics) , mathematical analysis
Since the risk of dietary inadequacy or excess is greater at the tails of the nutrient intake distributions than at the mean, marginal effects of explanatory variables estimated at the conditional mean using ordinary least squares may be of limited value in characterizing these distributions. Quantile regression is effective in this situation since it can estimate conditional functions at any part of the distribution. Quantile regression results suggest that age, education, and income have a larger influence at intake levels where the risk of excess is greater compared with intake levels where the risk of excess is lower.