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Binary Response Models

  • Joel L. Horowitz
Part of the Lecture Notes in Statistics book series (LNS, volume 131)

Abstract

This chapter is concerned with estimating the binary-response model
$$ Y = \left\{{\begin{array}{*{20}{c}} {1\;if\;Y* > 0\;}\\ {0\;otherwise} \end{array}} \right. $$
(3.1a)
where
$$ {Y^*} = X\beta+U $$
(3.1b)
Y is the observed dependent variable, X is a 1 × k vector of observed explanatory variables, β is a k × 1 vector of constant parameters, Y* is an unobserved, latent dependent variable, and U is an unobserved random variable. The inferential problem is to use observations of (Y, X) to estimate β and, to the extent possible, the probability that Y =1 conditional on X.

Keywords

Root Mean Square Error Maximum Score Asymptotic Distribution Asymptotic Bias Rejection Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Joel L. Horowitz
    • 1
  1. 1.Department of EconomicsUniversity of IowaIowa CityUSA

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