Small-Sample Properties of Estimators of Regression Coefficients Given a Common Pattern of Missing Data

Denis Conniffe

The Review of Economic Studies, 1983, vol. 50, issue 1, 111-120

Abstract: For a commonly occurring pattern of missing data, estimators of regression coefficients are derived by a non-likelihood method. The small-sample properties are investigated for the case of normality assumptions. The estimators are shown to be unbiased, exact small-sample variance formulae are derived, comparisons are made with ordinary least-squares estimators and it is demonstrated that the estimators can be more efficient than maximum-likelihood estimators in small samples.

Date: 1983
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.2307/2296958 (application/pdf)
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:oup:restud:v:50:y:1983:i:1:p:111-120.

Access Statistics for this article

The Review of Economic Studies is currently edited by Thomas Chaney, Xavier d’Haultfoeuille, Andrea Galeotti, Bård Harstad, Nir Jaimovich, Katrine Loken, Elias Papaioannou, Vincent Sterk and Noam Yuchtman

More articles in The Review of Economic Studies from Review of Economic Studies Ltd
Bibliographic data for series maintained by Oxford University Press ().