Binary Response Models: Logits, Probits and Semiparametrics | Zendy

Joël L. Horowitz | Zendy; N. E. Savin | Zendy

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Binary Response Models: Logits, Probits and Semiparametrics

Author(s) -

Joël L. Horowitz,

N. E. Savin

Publication year - 2001

Publication title -

the journal of economic perspectives

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 9.614

H-Index - 196

eISSN - 1944-7965

pISSN - 0895-3309

DOI - 10.1257/jep.15.4.43

Subject(s) - probit , probit model , logit , econometrics , nonparametric statistics , logistic regression , ordered probit , mathematics , statistics , semiparametric regression , contrast (vision) , computer science , artificial intelligence

A binary-response model is a mean-regression model in which the dependent variable takes only the values zero and one. This paper describes and illustrates the estimation of logit and probit binary-response models. The linear probability model is also discussed. Reasons for not using this model in applied research are explained and illustrated with data. Semiparametric and nonparametric models are also described. In contrast to logit and probit models, semi- and nonparametric models avoid the restrictive and unrealistic assumption that the analyst knows the functional form of the relation between the dependent variable and the explanatory variables.

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