Improved JIVE Estimators for Overidentified Linear Models with and without Heteroskedasticity

28 Pages Posted: 2 Dec 2008

See all articles by Daniel A. Ackerberg

Daniel A. Ackerberg

University of California, Los Angeles (UCLA) - Department of Economics

Paul J. Devereux

University College Dublin - Department of Economics; IZA Institute of Labor Economics

Date Written: August 2008

Abstract

We introduce two simple new variants of the Jackknife Instrumental Variables (JIVE) estimator for overidentified linear models and show that they are superior to the existing JIVE estimator, significantly improving on its small sample bias properties. We also compare our new estimators to existing Nagar (1959) type estimators. We show that, in models with heteroskedasticity, our estimators have superior properties to both the Nagar estimator and the related B2SLS estimator suggested in Donald and Newey (2001). These theoretical results are verified in a set of Monte-Carlo experiments and then applied to estimating the returns to schooling using actual data.

Keywords: JIVE, weak instruments

JEL Classification: L24, L40, O31, O34

Suggested Citation

Ackerberg, Daniel A. and Devereux, Paul J., Improved JIVE Estimators for Overidentified Linear Models with and without Heteroskedasticity (August 2008). CEPR Discussion Paper No. DP6926, Available at SSRN: https://ssrn.com/abstract=1307516

Daniel A. Ackerberg (Contact Author)

University of California, Los Angeles (UCLA) - Department of Economics ( email )

Box 951477
405 Hilgard Avenue
Los Angeles, CA 90095-1477
United States

Paul J. Devereux

University College Dublin - Department of Economics ( email )

Belfield
Dublin 4, 4
Ireland

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

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