Parameterization and Two-Stage Conditional Maximum Likelihood Estimation
- Creators
- Lien, Da-Hsiang Donald
- Vuong, Quang H.
Abstract
This paper considers the case where, after appropriate reparameterization, the probability density function can be factorized into a marginal density function and a conditional density function such that one of them involves fewer parameters. Then, two types of two-stage conditional maximum-likelihood estimators, 2SCMLEI and 2SCMLEII, can be considered according to whether the marginal or the conditional density has fewer parameters. Our first result indicates that, under some identification assumptions, there is a connection between the number of parameters in the marginal (or conditional) density functions under the two reparameterizations. Moreover, conditions for asymptotic equivalence and numerical equivalence between these two-stage estimators and the FIML estimator are obtained. Finally, examples are provided to illustrate our results.
Additional Information
This research was partially supported by National Science Foundation Grant SES-8410 593. We are greatly indebted to D. Rivers and J. Sobel for helpful discussion, and to Shoo Bee Doo for moral support. Remaining errors are ours.Attached Files
Submitted - sswp578.pdf
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Additional details
- Eprint ID
- 81491
- Resolver ID
- CaltechAUTHORS:20170915-134433552
- Created
-
2017-09-15Created 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
- 578