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On the Failure of the Bootstrap for Matching Estimators

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  • Imbens, Guido
  • Abadie, Alberto

Abstract

Matching estimators are widely used in empirical economics for the evaluation of programs or treatments. Researchers using matching methods often apply the bootstrap to calculate the standard errors. However, no formal justification has been provided for the use of the bootstrap in this setting. In this article, we show that the standard bootstrap is, in general, not valid for matching estimators, even in the simple case with a single continuous covariate where the estimator is root-N consistent and asymptotically normally distributed with zero asymptotic bias. Valid inferential methods in this setting are the analytic asymptotic variance estimator of Abadie and Imbens (2006a) as well as certain modifications of the standard bootstrap, like the subsampling methods in Politis and Romano (1994).

Suggested Citation

  • Imbens, Guido & Abadie, Alberto, 2008. "On the Failure of the Bootstrap for Matching Estimators," Scholarly Articles 3043415, Harvard University Department of Economics.
    • Handle: RePEc:hrv:faseco:3043415
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    1. David Myers & Robert B. Olsen & Neil Seftor & Julie Young & Christina Clark Tuttle, "undated". "The Impacts of Regular Upward Bound: Results from the Third Follow-Up Data Collection," Mathematica Policy Research Reports 145dc84be33e47e494cb5569f, Mathematica Policy Research.
    2. Michael Lechner, 2002. "Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 59-82, February.
    3. repec:mpr:mprres:4029 is not listed on IDEAS
    4. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    5. Jalan, Jyotsna & Ravallion, Martin, 1999. "Income gains to the poor from workfare - estimates for Argentina's TRABAJAR Program," Policy Research Working Paper Series 2149, The World Bank.
    6. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    7. Emanuela Galasso & Martin Ravallion, 2004. "Social Protection in a Crisis: Argentina's Plan Jefes y Jefas," The World Bank Economic Review, World Bank, vol. 18(3), pages 367-399.
    8. Patrick A. Puhani, 2002. "Advantage through Training in Poland? A Microeconometric Evaluation of the Employment Effects of Training and Job Subsidy Programmes," LABOUR, CEIS, vol. 16(3), pages 569-608, September.
    9. Kosuke Imai, 2005. "Do get-out-the-vote calls reduce turnout? The importance of statistical methods for field experiments," Natural Field Experiments 00272, The Field Experiments Website.
    10. Menno Pradhan & Laura B. Rawlings, 2002. "The Impact and Targeting of Social Infrastructure Investments: Lessons from the Nicaraguan Social Fund," The World Bank Economic Review, World Bank, vol. 16(2), pages 275-295, August.
    11. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc.
    12. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September.
    13. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    14. Imai, Kosuke, 2005. "Do Get-Out-the-Vote Calls Reduce Turnout? The Importance of Statistical Methods for Field Experiments," American Political Science Review, Cambridge University Press, vol. 99(2), pages 283-300, May.
    15. Barbara Sianesi, 2004. "An Evaluation of the Swedish System of Active Labor Market Programs in the 1990s," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 133-155, February.
    16. Roberto Agodini & Mark Dynarski, "undated". "Are Experiments the Only Option? A Look at Dropout Prevention Programs," Mathematica Policy Research Reports 51241adbf9fa4a26add6d54c5, Mathematica Policy Research.
    17. Roberto Agodini & Mark Dynarski, 2004. "Are Experiments the Only Option? A Look at Dropout Prevention Programs," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 180-194, February.
    18. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    19. Lorenzo Guarcello & Fabrizia Mealli & Furio Rosati, 2010. "Household vulnerability and child labor: the effect of shocks, credit rationing, and insurance," Journal of Population Economics, Springer;European Society for Population Economics, vol. 23(1), pages 169-198, January.
    20. Whitney K. Newey & Frank Windmeijer, 2005. "GMM with many weak moment conditions," CeMMAP working papers CWP18/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    22. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
    23. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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