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The case against JIVE

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  • Russell Davidson
  • James G. MacKinnon

Abstract

We perform an extensive series of Monte Carlo experiments to compare the performance of two variants of the ‘jackknife instrumental variables estimator’, or JIVE, with that of the more familiar 2SLS and LIML estimators. We find no evidence to suggest that JIVE should ever be used. It is always more dispersed than 2SLS, often very much so, and it is almost always inferior to LIML in all respects. Interestingly, JIVE seems to perform particularly badly when the instruments are weak. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Russell Davidson & James G. MacKinnon, 2006. "The case against JIVE," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 827-833, September.
  • Handle: RePEc:wly:japmet:v:21:y:2006:i:6:p:827-833
    DOI: 10.1002/jae.873
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    1. Russell Davidson & James G. MacKinnon, 2007. "Moments of IV and JIVE estimators," Econometrics Journal, Royal Economic Society, vol. 10(3), pages 541-553, November.
    2. 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.
    3. Nelson, Charles R & Startz, Richard, 1990. "Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator," Econometrica, Econometric Society, vol. 58(4), pages 967-976, July.
    4. 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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    6. 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.
    7. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    8. 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..
    9. 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.
    10. 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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    Cited by:

    1. Bekker, Paul A. & Crudu, Federico, 2012. "Symmetric Jackknife Instrumental Variable Estimation," MPRA Paper 37853, University Library of Munich, Germany.
    2. Russell Davidson & James G. MacKinnon, 2007. "Moments of IV and JIVE estimators," Econometrics Journal, Royal Economic Society, vol. 10(3), pages 541-553, November.
    3. Jeffrey R. Kling, 2006. "Incarceration Length, Employment, and Earnings," American Economic Review, American Economic Association, vol. 96(3), pages 863-876, June.
    4. Alexandre Dmitriev, 2013. "Institutions and growth: evidence from estimation methods robust to weak instruments," Applied Economics, Taylor & Francis Journals, vol. 45(13), pages 1625-1635, May.
    5. 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.
    6. 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(1), pages 42-86, February.
    7. Bekker, Paul A. & Crudu, Federico, 2015. "Jackknife instrumental variable estimation with heteroskedasticity," Journal of Econometrics, Elsevier, vol. 185(2), pages 332-342.
    8. Arcand, Jean-Louis & Ai, Chunrong & Ethier, Francois, 2007. "Moral hazard and Marshallian inefficiency: Evidence from Tunisia," Journal of Development Economics, Elsevier, vol. 83(2), pages 411-445, July.
    9. Emma M. Iglesias & Garry D. A. Phillips, 2012. "Almost Unbiased Estimation in Simultaneous Equation Models With Strong and/or Weak Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 505-520, June.
    10. Chambers, Marcus J., 2013. "Jackknife estimation of stationary autoregressive models," Journal of Econometrics, Elsevier, vol. 172(1), pages 142-157.
    11. Alfonso Flores-Lagunes, 2007. "Finite sample evidence of IV estimators under weak instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 677-694.
    12. Phillip, Garry & Xu, Yongdeng, 2016. "Almost Unbiased Variance Estimation in Simultaneous Equation Models," Cardiff Economics Working Papers E2016/10, Cardiff University, Cardiff Business School, Economics Section.
    13. Aiwei Huang & Madhurima Chandra & Laura Malkhasyan, 2021. "Weak Instrumental Variables: Limitations of Traditional 2SLS and Exploring Alternative Instrumental Variable Estimators," Papers 2104.12370, arXiv.org.
    14. Madsen, Jakob B. & Raschky, Paul A. & Skali, Ahmed, 2015. "Does democracy drive income in the world, 1500–2000?," European Economic Review, Elsevier, vol. 78(C), pages 175-195.
    15. Emanuele Millemaci & Ferdinando Ofria, 2014. "Kaldor-Verdoorn's law and increasing returns to scale: A comparison across developed countries," Journal of Economic Studies, Emerald Group Publishing, vol. 41(1), pages 140-162, January.
    16. Sören Blomquist & Matz Dahlberg, 2006. "The case against JIVE: a comment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 839-841, September.
    17. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    18. Millemaci, Emanuele & Ofria, Ferdinando, 2016. "Supply and demand-side determinants of productivity growth in Italian regions," Structural Change and Economic Dynamics, Elsevier, vol. 37(C), pages 138-146.
    19. Bhaven Sampat & Heidi L. Williams, 2019. "How Do Patents Affect Follow-On Innovation? Evidence from the Human Genome," American Economic Review, American Economic Association, vol. 109(1), pages 203-236, January.
    20. Phillips, Garry D.A. & Liu-Evans, Gareth, 2016. "Approximating and reducing bias in 2SLS estimation of dynamic simultaneous equation models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 734-762.
    21. MADSEN, Jakob B, 2018. "Is Inequality Increasing in r-g? The Dynamics of Capital’s Income Share in the UK, 1210-2013," Discussion paper series HIAS-E-70, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    22. Daniel A. Ackerberg & Paul J. Devereux, 2009. "Improved JIVE Estimators for Overidentified Linear Models with and without Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 91(2), pages 351-362, May.
    23. Yao, Yao & Ivanovski, Kris & Inekwe, John & Smyth, Russell, 2020. "Human capital and CO2 emissions in the long run," Energy Economics, Elsevier, vol. 91(C).
    24. Battiston, Diego, 2013. "The impact of immigration on the labour market: Evidence from 20 years of cross-border migration to Argentina," MPRA Paper 52424, University Library of Munich, Germany.

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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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