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Jackknife instrumental variables estimation in Stata


  • Brian P. Poi

    () (StataCorp)


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.

Suggested Citation

  • Brian P. Poi, 2006. "Jackknife instrumental variables estimation in Stata," Stata Journal, StataCorp LP, vol. 6(3), pages 364-376, September.
  • Handle: RePEc:tsj:stataj:v:6:y:2006:i:3:p:364-376

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    References listed on IDEAS

    1. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2003. "Instrumental variables and GMM: Estimation and testing," Stata Journal, StataCorp LP, vol. 3(1), pages 1-31, March.
    2. Marcelo J. Moreira & Brian P. Poi, 2003. "Implementing tests with correct size in the simultaneous equations model," Stata Journal, StataCorp LP, vol. 3(1), pages 57-70, March.
    3. 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..
    4. Nelson, Charles R & Startz, Richard, 1990. "The Distribution of the Instrumental Variables Estimator and Its t-Ratio When the Instrument Is a Poor One," The Journal of Business, University of Chicago Press, vol. 63(1), pages 125-140, January.
    5. 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..
    6. 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 518-529, October.
    7. Chao, John C. & Swanson, Norman R. & Hausman, Jerry A. & Newey, Whitney K. & Woutersen, Tiemen, 2012. "Asymptotic Distribution Of Jive In A Heteroskedastic Iv Regression With Many Instruments," Econometric Theory, Cambridge University Press, vol. 28(01), pages 42-86, February.
    8. Joshua D. Angrist & Alan B. Keueger, 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?," The Quarterly Journal of Economics, Oxford University Press, vol. 106(4), pages 979-1014.
    9. 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 219-228, February.
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    Cited by:

    1. Anikó Bíró, 2013. "Subjective mortality hazard shocks and the adjustment of consumption expenditures," Journal of Population Economics, Springer;European Society for Population Economics, vol. 26(4), pages 1379-1408, October.
    2. Austin Nichols, 2007. "Causal inference with observational data," Stata Journal, StataCorp LP, vol. 7(4), pages 507-541, December.
    3. repec:eee:touman:v:65:y:2018:i:c:p:212-223 is not listed on IDEAS


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