Misspecification and Conditional Maximum Likelihood Estimation
- Creators
- Vuong, Quang H.
Abstract
Recently White (1982) studied the properties of Maximum Likelihood estimation of possibly misspecified models. The present paper extends Andersen (1970) results on Conditional Maximum Likelihood estimators (CMLE) to such a situation. In particular, the asymptotic properties of CMLE's are derived under correct and incorrect specification of the conditional model. Robustness of conditional inferences and estimation with respect to misspecification of the model for the conditioning variables is emphasized. Conditions for asymptotic efficiency of CMLE's are obtained, and specification tests a la Hausman (1978) and White (1982) are derived. Examples are also given to illustrate the use of CMLE's properties. These examples include the simple linear model, the multinomial logit model, the simple Tobit model, and the multivariate logit model.
Additional Information
Revised. Original dated to April 1983. I am much indebted to J. Dubin, R. Engle, D. Grether, J. Link, D. Rivers, and H. White for helpful comments and criticism. Remaining errors are of course mine.Attached Files
Submitted - sswp503_-_revised.pdf
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Additional details
- Eprint ID
- 81658
- Resolver ID
- CaltechAUTHORS:20170920-161718102
- Created
-
2017-09-20Created from EPrint's datestamp field
- Updated
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2019-10-03Created from EPrint's last_modified field
- Caltech groups
- Social Science Working Papers
- Series Name
- Social Science Working Paper
- Series Volume or Issue Number
- 503