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Split-Sample Instrumental Variables Estimates of the Return to Schooling

Joshua Angrist () and Alan Krueger

Journal of Business & Economic Statistics, 1995, vol. 13, issue 2, 225-35

Abstract: 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.

Date: 1995
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Handle: RePEc:bes:jnlbes:v:13:y:1995:i:2:p:225-35