Split-Sample Instrumental Variables Estimates of the Return to Schooling
Joshua Angrist () and
Journal of Business & Economic Statistics, 1995, vol. 13, issue 2, 225-35
Two-stage least squares is biased in the same direction as ordinary least squares even in very large samples. The authors propose a split-sample instrumental variables estimator that is not biased toward ordinary least squares. Split-sample instrumental variables uses one-half of a sample to estimate parameters of the first-stage equation. Estimated first-stage parameters are then used to construct fitted values and second-stage parameter estimates in the other half sample. Split-sample instrumental variables is biased toward zero but this bias can be corrected. The authors use split-sample estimators to reexamine instrumental variables and two-stage least squares estimates of the returns to schooling.
References: Add references at CitEc
Citations: View citations in EconPapers (139) Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:13:y:1995:i:2:p:225-35
Ordering information: This journal article can be ordered from
Access Statistics for this article
Journal of Business & Economic Statistics is currently edited by Jonathan H. Wright and Keisuke Hirano
More articles in Journal of Business & Economic Statistics from American Statistical Association
Bibliographic data for series maintained by Christopher F. Baum ().