A Minimum Power Divergence Class of CDFs and Estimators for Binary Choice Models
| dc.creator | Mittelhammer, Ronald C. | |
| dc.creator | Judge, George G. | |
| dc.date | 2017-04-01T14:09:54Z | |
| dc.date.accessioned | 2026-07-09T04:29:22Z | |
| dc.description | The Cressie-Read (CR) family of power divergence measures is used to identify a new class of statistical models and estimators for competing explanations of the data in binary choice models. A large flexible class of cumulative distribution functions and associated probability density functions emerge that subsumes the conventional logit model, and forms the basis for a large set of estimation alternatives to traditional logit and probit methods. Asymptotic properties of estimators are identified, and sampling experiments are used to provide a basis for gauging the finite sample performance of the estimators in this new class of statistical models. | |
| dc.identifier | doi:10.22004/ag.econ.37759 | |
| dc.identifier | https://ageconsearch.umn.edu/record/37759/files/CUDARE%201059%20Mittelhammer%20and%20Judge.pdf | |
| dc.identifier | http://ageconsearch.umn.edu/record/37759 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/550682 | |
| dc.language | eng | |
| dc.publisher | ||
| dc.source | http://ageconsearch.umn.edu/record/37759 | |
| dc.title | A Minimum Power Divergence Class of CDFs and Estimators for Binary Choice Models | |
| dc.type | Text |