Seminonparametric Estimation of Binary-Choice Models with an Application to Labor-Force Participation

Siegfried Gabler, Francois Laisney and Michael Lechner

Journal of Business & Economic Statistics, 1993, vol. 11, issue 1, 61-80

Abstract: The authors adapt the estimation method proposed by A. R. Gallant and D. N. Nychka (1987) to binary-choice models. They present Monte Carlo and asymptotic comparisons with the probit estimator and discuss optimization algorithms, choice of starting values, and strategies f or choosing the number of parameters used in approximating the density. Seminonparametric estimation is almost as efficient as probit estimation in normal samples and performs better in nonnormal sample s. The estimation of a participation model with 3,658 observations and 21 explanatory variables demonstrates the practicability of this approa ch on personal computers.

Date: 1993
References: Add references at CitEc
Citations: View citations in EconPapers (64)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:11:y:1993:i:1:p:61-80

Ordering information: This journal article can be ordered from
http://www.amstat.org/publications/index.html

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Jonathan H. Wright and Keisuke Hirano

More articles in Journal of Business & Economic Statistics from American Statistical Association
Bibliographic data for series maintained by Christopher F. Baum ().