Operational characteristics of maximum score estimation

Charles F. Manski*, T. Scott Thompson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

Abstract

This paper reports on the operational characteristics of maximum score estimation of a linear model from binary response data. A series of previous articles have shown that in theory the maximum score method makes possible binary response analysis under very weak distributional assumptions. Here, we present evidence on the properties of maximum score estimation in practice. After reviewing the known asymptotic theory of maximum score estimation, the paper describes an algorithm for maximum score estimation and characterizes its performance. Then findings from a Monte Carlo study comparing maximum score and logit maximum likelihood estimation are reported. Finally, the accuracy of bootstrap estimation of maximum score root mean square errors is evaluated.

Original languageEnglish (US)
Pages (from-to)85-108
Number of pages24
JournalJournal of Econometrics
Volume32
Issue number1
DOIs
StatePublished - Jun 1986

Funding

*This research was supported under National Science Foundation Grant SES-8319335 and by a grant from the University of Wisconsin Graduate School. Computational facilities were provided by the Center for Demography and Ecology of the University of Wisconsin.

ASJC Scopus subject areas

  • Economics and Econometrics

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