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Improved JIVE Estimators for Overidentified Linear Models with and without Heteroskedasticity

Daniel Ackerberg () and Paul Devereux ()

The Review of Economics and Statistics, 2009, vol. 91, issue 2, 351-362

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. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Date: 2009
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Working Paper: Improved JIVE Estimators for Overidentified Linear Models with and without Heteroskedasticity (2008) Downloads
Working Paper: Improved Jive estimators for overidentified linear models with and without heteroskedasticity (2008) Downloads
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The Review of Economics and Statistics is currently edited by Amitabh Chandra, Olivier Coibion, Bryan S. Graham, Shachar Kariv, Amit K. Khandelwal, Asim Ijaz Khwaja, Brigitte C. Madrian and Rohini Pande

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