The econometrics of piecewise-linear budget constraints: A monte carlo study

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)243-248
Number of pages6
JournalJournal of Business and Economic Statistics
Volume5
Issue number2
DOIs
StatePublished - 1987

Fingerprint

Budget Constraint
Ordinary Least Squares
Monte Carlo Study
Linear Constraints
Econometrics
Piecewise Linear
econometrics
budget
Ordinary Least Squares Estimator
Decrease
statistical test
demand
Statistical test
Maximum Likelihood Estimator
Maximum Likelihood
Sample Size
Estimator
income
Alternatives
Evaluation

Keywords

  • Alternative estimator performance
  • Monte carlo experiments

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty
  • Social Sciences (miscellaneous)

Cite this

The econometrics of piecewise-linear budget constraints : A monte carlo study. / Megdal, Sharon B.

In: Journal of Business and Economic Statistics, Vol. 5, No. 2, 1987, p. 243-248.

Research output: Contribution to journalArticle

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