On Least Squares Estimation When the Dependent Variable is Grouped

Mark Stewart

No 539, Working Papers from Princeton University, Department of Economics, Industrial Relations Section.

Abstract: This paper examines the problem of estimating the parameters of an underlying linear model using data in which the dependent variable is only observed to fall in a certain interval on a continuous scale, its actual value remaining unobserved. A Least Squares algorithm for attaining the Maximum Likelihood estimator is described, the asymptotic bias of the OLS estimator derived for the normal regressors case and a "moment" estimator presented. A "two-step estimator" based on combining the two approaches is proposed and found to perform well in both an economic illustration and simulation experiments.

JEL-codes: M49 (search for similar items in EconPapers)
Date: 1982-11
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: On Least Squares Estimation when the Dependent Variable is Grouped (1983)
Working Paper: On Least Squares Estimation when the Dependent Variable is Grouped (1982)
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Persistent link: https://EconPapers.repec.org/RePEc:pri:indrel:159

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