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Improved Jive Estimators for Overidentified Linear Models with and without Heteroskedasticity

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  • Daniel A. Ackerberg

    (UCLA)

  • Paul J. Devereux

    (University College Dublin)

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, signifi- cantly 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.

Suggested Citation

  • Daniel A. Ackerberg & Paul J. Devereux, 2008. "Improved Jive Estimators for Overidentified Linear Models with and without Heteroskedasticity," Working Papers 200817, School of Economics, University College Dublin.
    • Handle: RePEc:ucn:wpaper:200817
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    References listed on IDEAS

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    6. Daniel A. Ackerberg & Paul J. Devereux, 2006. "Comment on ‘The case against JIVE’," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 835-838, September.
    7. Jinyong Hahn & Jerry Hausman & Guido Kuersteiner, 2004. "Estimation with weak instruments: Accuracy of higher-order bias and MSE approximations," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 272-306, June.
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    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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