IDEAS home Printed from https://ideas.repec.org/p/fri/fribow/fribow00466.html
   My bibliography  Save this paper

The finite sample performance of inference methods for propensity score matching and weighting estimators

Author

Listed:
  • Bodory, Hugo
  • Huber, Martin
  • Camponovo, Lorenzo
  • Lechner, Michael

Abstract

This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation design, which is based on large scale labor market data from Germany and varies w.r.t. treatment selectivity, effect heterogeneity, the share of treated, and the sample size. The results suggest that in general, the bootstrap procedures dominate the asymptotic ones in terms of size and power for both matching and weighting estimators. Furthermore, the results are qualitatively quite robust across the various simulation features.

Suggested Citation

  • Bodory, Hugo & Huber, Martin & Camponovo, Lorenzo & Lechner, Michael, 2016. "The finite sample performance of inference methods for propensity score matching and weighting estimators," FSES Working Papers 466, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
  • Handle: RePEc:fri:fribow:fribow00466
    as

    Download full text from publisher

    File URL: http://doc.rero.ch/record/258214/files/WP_SES_466.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
    2. 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.
    3. Michael Lechner, 2002. "Program Heterogeneity And Propensity Score Matching: An Application To The Evaluation Of Active Labor Market Policies," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 205-220, May.
    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. JAMES G. MacKINNON, 2006. "Bootstrap Methods in Econometrics," The Economic Record, The Economic Society of Australia, vol. 82(s1), pages 2-18, September.
    6. Hugo Ñopo, 2008. "Matching as a Tool to Decompose Wage Gaps," The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 290-299, May.
    7. Martin Huber & Michael Lechner & Giovanni Mellace, 2016. "The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 139-160, January.
    8. Lechner, Michael & Strittmatter, Anthony, 2014. "Practical Procedures to Deal with Common Support Problems in Matching Estimation," Economics Working Paper Series 1410, University of St. Gallen, School of Economics and Political Science.
    9. Newey, Whitney K., 1984. "A method of moments interpretation of sequential estimators," Economics Letters, Elsevier, vol. 14(2-3), pages 201-206.
    10. Busso, Matias & DiNardo, John & McCrary, Justin, 2009. "New Evidence on the Finite Sample Properties of Propensity Score Matching and Reweighting Estimators," IZA Discussion Papers 3998, Institute for the Study of Labor (IZA).
    11. Shakeeb Khan & Elie Tamer, 2010. "Irregular Identification, Support Conditions, and Inverse Weight Estimation," Econometrica, Econometric Society, vol. 78(6), pages 2021-2042, November.
    12. Racine, Jeffrey S. & MacKinnon, James G., 2007. "Inference via kernel smoothing of bootstrap P values," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5949-5957, August.
    13. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    14. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 1053-1079.
    15. 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.
    16. Kosuke Imai & Marc Ratkovic, 2014. "Covariate balancing propensity score," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 243-263, January.
    17. Martin Huber & Michael Lechner & Andreas Steinmayr, 2015. "Radius matching on the propensity score with bias adjustment: tuning parameters and finite sample behaviour," Empirical Economics, Springer, vol. 49(1), pages 1-31, August.
    18. Rothe, Christoph & Firpo, Sergio, 2013. "Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions," IZA Discussion Papers 7564, Institute for the Study of Labor (IZA).
    19. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    20. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
    21. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," Review of Economic Studies, Oxford University Press, vol. 65(2), pages 261-294.
    22. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(03), pages 199-236, June.
    23. Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Krumer, Alex & Lechner, Michael, 2016. "Midweek Effect on Performance: Evidence from the German Soccer Bundesliga," Economics Working Paper Series 1609, University of St. Gallen, School of Economics and Political Science.
    2. repec:eee:csdana:v:115:y:2017:i:c:p:91-102 is not listed on IDEAS
    3. 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.
    4. repec:eee:jimfin:v:77:y:2017:i:c:p:143-163 is not listed on IDEAS
    5. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, pages 91-102.
    6. Anthony Strittmatter & Michael Lechner, 2017. "Sorting on the Used-Car Market After the Volkswagen Emission Scandal," CESifo Working Paper Series 6480, CESifo Group Munich.
    7. repec:eee:eecrev:v:100:y:2017:i:c:p:412-427 is not listed on IDEAS
    8. Krumer, Alex & Lechner, Michael, 2016. "First In First Win: Evidence on Unfairness of Round-Robin Tournaments in Mega-Events," Economics Working Paper Series 1611, University of St. Gallen, School of Economics and Political Science.

    More about this item

    Keywords

    inference; variance estimation; treatment effects; matching; inverse probability weighting;

    JEL classification:

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fri:fribow:fribow00466. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ivo raemy). General contact details of provider: http://edirc.repec.org/data/wsffrch.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.