TY - JOUR
T1 - The econometrics of piecewise-linear budget constraints
T2 - A monte carlo study
AU - Megdal, Sharon Bernstein
N1 - Funding Information:
I would like to thank the College of Business and Public Administration of the University of Arizona for a research grant that supported this research; Lester D. Taylor, Paul Ruud, and Robert Moffitt for helpful suggestions; and Edward Sloat, Darcy Della Flora, and Deborah Barker for research assistance.
PY - 1987/4
Y1 - 1987/4
N2 - This article presents a Monte Carlo evaluation of some alternative estimators for a demand model when the budget constraint is piecewise-linear and the budget set is convex. We examine the performance of two maximum likelihood (ML) estimators and an ordinary least squares (OLS) estimator under varying sample sizes and error variances. A simple log-linear demand function, with income and price as the explanatory variables, is specified. Although I find that the OLS bias decreases as the error variance decreases, the ML results are far superior. Furthermore, statistical tests based on the OLS results lead to erroneous conclusions regarding the structure.
AB - This article presents a Monte Carlo evaluation of some alternative estimators for a demand model when the budget constraint is piecewise-linear and the budget set is convex. We examine the performance of two maximum likelihood (ML) estimators and an ordinary least squares (OLS) estimator under varying sample sizes and error variances. A simple log-linear demand function, with income and price as the explanatory variables, is specified. Although I find that the OLS bias decreases as the error variance decreases, the ML results are far superior. Furthermore, statistical tests based on the OLS results lead to erroneous conclusions regarding the structure.
KW - Alternative estimator performance
KW - Monte carlo experiments
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U2 - 10.1080/07350015.1987.10509582
DO - 10.1080/07350015.1987.10509582
M3 - Article
AN - SCOPUS:0040497140
SN - 0735-0015
VL - 5
SP - 243
EP - 248
JO - Journal of Business and Economic Statistics
JF - Journal of Business and Economic Statistics
IS - 2
ER -