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Jackknife instrumental variables estimation in Stata

  • Brian P. Poi

    ()

    (StataCorp)

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    The two-stage least-squares (2SLS) instrumental variables estimator is commonly used to address endogeneity. However, the estimator suffers from bias that is exacerbated when the instruments are only weakly correlated with the en- dogenous variables and when many instruments are used. In this article, I discuss jackknife instrumental variables estimation as an alternative to 2SLS. Monte Carlo simulations comparing the jackknife instrument variables estimators to 2SLS and limited information maximum likelihood (LIML) show that two of the four vari- ants perform remarkably well even when 2SLS does not. In a weak-instrument experiment, the two best performing jackknife estimators also outperform LIML. Copyright 2006 by StataCorp LP.

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    Article provided by StataCorp LP in its journal Stata Journal.

    Volume (Year): 6 (2006)
    Issue (Month): 3 (September)
    Pages: 364-376

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    Handle: RePEc:tsj:stataj:v:6:y:2006:i:3:p:364-376
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    1. Joshua Angrist & Alan Krueger, 1990. "Does Compulsory School Attendance Affect Schooling and Earnings?," Working Papers 653, Princeton University, Department of Economics, Industrial Relations Section..
    2. Marcelo J. Moreira & Brian P. Poi, 2003. "Implementing Tests with Correct Size in the Simultaneous Equation Model," Harvard Institute of Economic Research Working Papers 1993, Harvard - Institute of Economic Research.
    3. 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..
    4. Nelson, C. & Startz, R., 1988. "The Distribution Of The Instrumental Variables Estimator And Its T-Ratio When The Instrument Is A Poor One," Working Papers 88-07, University of Washington, Department of Economics.
    5. Phillips, Garry D A & Hale, C, 1977. "The Bias of Instrumental Variable Estimators of Simultaneous Equation Systems," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(1), pages 219-28, February.
    6. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2002. "Instrumental variables and GMM: Estimation and testing," United Kingdom Stata Users' Group Meetings 2003 02, Stata Users Group.
    7. Chao & Swanson & Hausman & Newey & Woutersen, 2010. "Asymptotic Distribution of JIVE in a Heteroskedastic IV Regression with Many Instruments," Economics Working Paper Archive 567, The Johns Hopkins University,Department of Economics.
    8. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-29, October.
    9. 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..
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