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Bootstrap inference of matching estimators for average treatment effects

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  • Taisuke Otsu
  • Yoshiyasu Rai

Abstract

Abadie and Imbens (2008) showed that the standard naive bootstrap is inconsistent to estimate the distribution of the matching estimator for treatment effects with a fixed number of matches. This article proposes an asymptotically valid inference method for the matching estimators based on the wild bootstrap. The key idea is to resample not only the regression residuals of treated and untreated observations but also the ones to estimate the average treatment effects. The proposed method is valid even for the case of vector covariates by incorporating the bias correction method in Abadie and Imbens (2011), and is applicable to estimate the average treatment effect and the counterpart for the treated population. A simulation study indicates that our wild bootstrap method is favorably comparable to the asymptotic normal approximation. As an empirical illustration, we apply our bootstrap method to the National Supported Work data.

Suggested Citation

  • Taisuke Otsu & Yoshiyasu Rai, 2015. "Bootstrap inference of matching estimators for average treatment effects," STICERD - Econometrics Paper Series /2015/580, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stiecm:/2015/580
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    Cited by:

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    3. Shu Yang & Yunshu Zhang, 2023. "Multiply robust matching estimators of average and quantile treatment effects," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 235-265, March.
    4. Cerqua, A. & Ferrante, C. & Letta, M., 2021. "Electoral Earthquake: Natural Disasters and the Geography of Discontent," GLO Discussion Paper Series 790, Global Labor Organization (GLO).
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    6. Wagner, Gary A. & Rork, Jonathan C., 2023. "Does state tax reciprocity affect interstate commuting? Evidence from a natural experiment," Regional Science and Urban Economics, Elsevier, vol. 102(C).
    7. Ferman, Bruno, 2021. "Matching estimators with few treated and many control observations," Journal of Econometrics, Elsevier, vol. 225(2), pages 295-307.
    8. García, Gustavo A. & Ramírez-Hassan, Andrés & Saravia, Estefanía & Vargas, Raquel & Duque, Juan Fernando & Londoño, Daniel, 2022. "Impacto de las intervenciones físicas en el transporte público en Medellín (Colombia) como herramientas para reducir la exclusión social," IDB Publications (Working Papers) 12014, Inter-American Development Bank.
    9. Hugo Bodory & Lorenzo Camponovo & Martin Huber & Michael Lechner, 2020. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 183-200, January.
    10. Andr'es Ram'irez-Hassan & Raquel Vargas-Correa & Gustavo Garc'ia & Daniel Londo~no, 2020. "Optimal selection of the number of control units in kNN algorithm to estimate average treatment effects," Papers 2008.06564, arXiv.org.
    11. Matthew Blackwell & Anton Strezhnev, 2022. "Telescope matching for reducing model dependence in the estimation of the effects of time‐varying treatments: An application to negative advertising," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 377-399, January.
    12. D’Arcangelo, Filippo Maria & Pavan, Giulia & Calligaris, Sara, 2022. "The Impact of the European Carbon Market on Firm Productivity: Evidence from Italian Manufacturing Firms," FEEM Working Papers 324170, Fondazione Eni Enrico Mattei (FEEM).
    13. Filippo Maria D’Arcangelo & Giulia Pavan & Sara Calligaris, 2022. "The Impact of the European Carbon Market on Firm Productivity: Evidence from Italian Manufacturing Firms," Working Papers 2022.24, Fondazione Eni Enrico Mattei.
    14. Cristina Bernini & Augusto Cerqua, 2020. "Are eco‐labels good for the local economy?," Papers in Regional Science, Wiley Blackwell, vol. 99(3), pages 645-661, June.
    15. Taisuke Otsu & Mengshan Xu, 2022. "Isotonic propensity score matching," STICERD - Econometrics Paper Series 623, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    16. Bernini, Cristina & Cerqua, Augusto, 2019. "Do sustainability policies finance local economies?," MPRA Paper 91882, University Library of Munich, Germany.
    17. Cerqua, Augusto & Ferrante, Chiara & Letta, Marco, 2023. "Electoral earthquake: Local shocks and authoritarian voting," European Economic Review, Elsevier, vol. 156(C).
    18. Huber, Martin & Camponovo, Lorenzo & Bodory, Hugo & Lechner, Michael, 2016. "A wild bootstrap algorithm for propensity score matching estimators," FSES Working Papers 470, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    19. Mengshan Xu & Taisuke Otsu, 2022. "Isotonic propensity score matching," Papers 2207.08868, arXiv.org.

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    More about this item

    Keywords

    Treatment effect; matching; bootstrap;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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