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Practical Procedures to Deal with Common Support Problems in Matching Estimation

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  • Lechner, Michael

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  • Strittmatter, Anthony

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Abstract

This paper assesses the performance of common estimators adjusting for differences in covariates, like matching and regression, when faced with so-called common support problems. It also shows how different procedures suggested in the literature to tackle common support problems affect the properties of such estimators. Based on an Empirical Monte Carlo simulation design, a lack of common support is found to increase the root mean squared error (RMSE) of all investigated parametric and semiparametric estimators. Dropping observa¬tions that are off support usually improves their performance, although the amount of improvement depends on the particular method used.

Suggested Citation

  • 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.
  • Handle: RePEc:usg:econwp:2014:10
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    References listed on IDEAS

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    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
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    5. repec:ags:stataj:116250 is not listed on IDEAS
    6. Martin Biewen & Bernd Fitzenberger & Aderonke Osikominu & Marie Paul, 2014. "The Effectiveness of Public-Sponsored Training Revisited: The Importance of Data and Methodological Choices," Journal of Labor Economics, University of Chicago Press, vol. 32(4), pages 837-897.
    7. Matias Busso & John DiNardo & Justin McCrary, 2014. "New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 885-897, December.
    8. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    9. Shakeeb Khan & Elie Tamer, 2010. "Irregular Identification, Support Conditions, and Inverse Weight Estimation," Econometrica, Econometric Society, vol. 78(6), pages 2021-2042, November.
    10. Abadie, Alberto & Drukker, David M. & Herr, Jane Leber & Imbens, Guido W., 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 1-22.
    11. Guido Imbens & Karthik Kalyanaraman, 2012. "Optimal Bandwidth Choice for the Regression Discontinuity Estimator," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 933-959.
    12. Annabelle Doerr & Bernd Fitzenberger & Thomas Kruppe & Marie Paul & Anthony Strittmatter, 2017. "Employment and Earnings Effects of Awarding Training Vouchers in Germany," ILR Review, Cornell University, ILR School, vol. 70(3), pages 767-812, May.
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    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. 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.
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    Citations

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    Cited by:

    1. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
    2. Annabelle Doerr & Bernd Fitzenberger & Thomas Kruppe & Marie Paul & Anthony Strittmatter, 2017. "Employment and Earnings Effects of Awarding Training Vouchers in Germany," ILR Review, Cornell University, ILR School, vol. 70(3), pages 767-812, May.
    3. Michael C. Knaus & Steffen Otterbach, 2019. "Work Hour Mismatch And Job Mobility: Adjustment Channels And Resolution Rates," Economic Inquiry, Western Economic Association International, vol. 57(1), pages 227-242, January.
    4. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Papers 1810.13237, arXiv.org, revised Dec 2018.
    5. Arun Advani & Toru Kitagawa & Tymon S{l}oczy'nski, 2018. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," Papers 1809.09527, arXiv.org, revised Apr 2019.
    6. Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," IZA Discussion Papers 10961, Institute of Labor Economics (IZA).
    7. Advani, Arun & Sloczynski, Tymon, 2013. "Mostly Harmless Simulations? On the Internal Validity of Empirical Monte Carlo Studies," IZA Discussion Papers 7874, Institute of Labor Economics (IZA).
    8. 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.
    9. repec:eee:csdana:v:115:y:2017:i:c:p:91-102 is not listed on IDEAS
    10. Knaus, Michael C., 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," IZA Discussion Papers 11547, Institute of Labor Economics (IZA).
    11. Andrea Albanese & Bart Cockx & Yannick Thuy, 2015. "Working Time Reductions at the End of the Career. Do they prolong the Time Spent in Employment?," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2015024, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    12. 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, vol. 115(C), pages 91-102.
    13. Doerr, Annabelle & Strittmatter, Anthony, 2014. "Assignment Mechanisms, Selection Criteria, and the Effectiveness of Training Programs," Economics Working Paper Series 1421, University of St. Gallen, School of Economics and Political Science, revised May 2017.
    14. Anthony Strittmatter, 2018. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," Papers 1812.06533, arXiv.org, revised Mar 2019.
    15. Andrea Albanese & Lorenzo Cappellari & Marco Leonardi, 2017. "The Effects of Youth Labor Market Reforms: Evidence from Italian Apprenticeships," CESifo Working Paper Series 6481, CESifo Group Munich.
    16. Doerr, Annabelle, 2017. "Back to work: The Long-term Effects of Vocational Training for Female Job Returners," Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168213, Verein für Socialpolitik / German Economic Association.
    17. Bodory, Hugo & Camponovo, Lorenzo & Huber, Martin & Lechner, Michael, 2016. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," IZA Discussion Papers 9706, Institute of Labor Economics (IZA).
    18. Anthony Strittmatter & Michael Lechner, 2017. "Sorting on the Used-Car Market After the Volkswagen Emission Scandal," CESifo Working Paper Series 6480, CESifo Group Munich.

    More about this item

    Keywords

    Empirical Monte Carlo Study; matching estimation; regression; common support; outlier; small sample performance;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy

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