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Improved JIVE Estimators for Overidentified Linear Models with and without Heteroskedasticity

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Author Info
Ackerberg, Daniel
Devereux, Paul J.

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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.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 6926.

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Date of creation: Aug 2008
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Handle: RePEc:cpr:ceprdp:6926

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Keywords: JIVE; weak instruments;

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. James G. MacKinnon & Russell Davidson, 2006. "The case against JIVE," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 827-833. [Downloadable!]
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  2. Angrist, Joshua D & Krueger, Alan B, 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?," The Quarterly Journal of Economics, MIT Press, vol. 106(4), pages 979-1014, November. [Downloadable!] (restricted)
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  3. Paul J. Devereux & Daniel A. Ackerberg, 2006. "Comment on 'The case against JIVE'," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 835-838. [Downloadable!]
  4. Jerry Hausman & Whitney Newey & Tiemen Woutersen & John Chao & Norman Swanson, 2007. "Instrumental variable estimation with heteroskedasticity and many instruments," CeMMAP working papers CWP22/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
  5. John Chao & Norman Swanson, 2004. "Estimation and Testing Using Jackknife IV in Heteroskedastic Regressions With Many Weak Instruments," Departmental Working Papers 200420, Rutgers University, Department of Economics. [Downloadable!]
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  6. 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, 06. [Downloadable!] (restricted)
  7. Paul J. Devereux, 2007. "Small-sample bias in synthetic cohort models of labor supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 839-848. [Downloadable!]
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  8. Blomquist, Soren & Dahlberg, Matz, 1999. "Small Sample Properties of LIML and Jackknife IV Estimators: Experiments with Weak Instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(1), pages 69-88, Jan.-Feb.. [Downloadable!]
  9. Angrist, J D & Imbens, G W & Krueger, A B, 1999. "Jackknife Instrumental Variables Estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(1), pages 57-67, Jan.-Feb.. [Downloadable!]
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  10. Angrist, Joshua D & Krueger, Alan B, 1995. "Split-Sample Instrumental Variables Estimates of the Return to Schooling," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 225-35, April.
  11. Devereux, Paul J., 2007. "Improved Errors-in-Variables Estimators for Grouped Data," CEPR Discussion Papers 6167, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  1. Devereux, Paul J., 2007. "Improved Errors-in-Variables Estimators for Grouped Data," CEPR Discussion Papers 6167, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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