English, Article, Journal or magazine article edition: Identification & Information in Monotone Binary Models Thierry Magnac; Eric Maurin

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Bookmark: http://trove.nla.gov.au/version/12064
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Identification &​ Information in Monotone Binary Models
Author
  • Thierry Magnac
  • Eric Maurin
Physical Description
  • preprint
Notes
  • Let, y, a binary outcome, v a continuous explanatory variable and x some other explanatory variables. We study inference on the parameter b of the semiparametric binary regression model y=​1(xb+v+e>0). We show that the set-up introduced by Lewbel (2000) that is, an uncorrelated-error restriction (E(x'e)=​0) combined with a partial-independance assumption (F(e/​v,x)=​F(e/​x)) and a large support assumption (Supp(-xb-e) c Supp(v)) provides exact identification of b and F(e/​x). The two restrictions that the population distribution of the random variable w=​(y,v,x) should satisfy are Monotone (1) and Large Support (2) conditions: (1) E(y/​v,x) is monotone in v and (2) E(y/​v,x) varies from 0 to 1 when v varies over its support. Moreover, we show that Lewbel's moment estimator attains the semi-parametric efficiency bound in the set of latent models that he considers. Yet, the uncorrelated-error and partial-independence assumptions are not sufficient to identify b when the support of v is not sufficiently rich. We propose intuitive additional restrictions on the tails of the conditional distribution of e under which b remains exactly identified even when condition(2) is not satisfied. In such a case, Monte-Carlo experiments show that the estimation performs well in moderately small samples. An extension to ordered choice models is provided.
  • Binary models, semiparametric methods, efficiency bounds
  • RePEc:lea:leawpi:0309
Language
  • English
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