Unconditional pseudo-maximum likelihood and adaptive estimation in the presence of conditional heterogeneity of unknown form

Douglas Hodgson ()

Econometric Reviews, 2000, vol. 19, issue 2, 175-206

Abstract: We consider parametric non-linear regression models with additive innovations which are serially uncorrelated but not necessarily independent, and consider the consequences of maximum likelihood and related one-step iterative estimation when the innovations are treated as being iid from their unconditional density. We find that the estimators' asymptotic covariance matrices will generally differ from those that would obtain if the errors actually were iid, except for the special case of strictly exogenous regressors. One important application of these results is to analysis of the properties of adaptive estimators, which employ nonparametric kernel estimates of the unconditional density of the disturbances in the construction of one-step iterative estimators. In the presence of strictly exogenous regressors, adaptive estimators are found to be asymptotically equivalent to the one-step iterative estimators that use the correct unconditional density. We illustrate our results through a brief Monte Carlo study.

Date: 2000
References: Add references at CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/07474930008800467 (text/html)
Access to full text is restricted to subscribers.

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:taf:emetrv:v:19:y:2000:i:2:p:175-206

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474930008800467

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().