Partially Adaptive Econometric Methods and the Modern Obesity Epidemic

36 Pages Posted: 21 Jul 2018

See all articles by Scott A. Carson

Scott A. Carson

University of Texas of the Permian Basin; CESifo (Center for Economic Studies and Ifo Institute)

James McDonald

Brigham Young University

Date Written: June 01, 2018

Abstract

Assumptions about explanatory variables and errors are central in regression analysis. For example, the well-known method of ordinary least squares yields consistent and efficient estimators if the underlying error terms are independently, identically, and normally distributed. Additionally, the conditional distribution of the dependent variable is symmetric. The modern obesity epidemic is a well-known health dilemma where the BMI distribution was initially positively skewed but has become more symmetric, which may affect inferences about health and public resource allocation. This study applies partially adaptive estimation methods with flexible error distributions to account for possible skewness and leptokurtosis in the distribution of BMI.

Keywords: obesity epidemic, partially adaptive estimation, skewed generalized T distribution

JEL Classification: C100, D130, J130

Suggested Citation

Carson, Scott A. and McDonald, James B., Partially Adaptive Econometric Methods and the Modern Obesity Epidemic (June 01, 2018). CESifo Working Paper Series No. 7058, Available at SSRN: https://ssrn.com/abstract=3210553

Scott A. Carson (Contact Author)

University of Texas of the Permian Basin ( email )

4901 East University
Odessa, TX 79762
United States

CESifo (Center for Economic Studies and Ifo Institute)

Poschinger Str. 5
Munich, DE-81679
Germany

James B. McDonald

Brigham Young University ( email )

130 Faculty Office Bldg.
Provo, UT 84602-2363
United States
801-378-3463 (Phone)

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