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Practical Procedures to Deal with Common Support Problems in Matching Estimation

Author

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  • Lechner, Michael

    (University of St. Gallen)

  • Strittmatter, Anthony

    (University of St. Gallen)

Abstract

This paper assesses the performance of common estimators adjusting for differences in covariates, such as matching and regression, when faced with so-called common support problems. It also shows how different procedures suggested in the literature 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 observations that are off support usually improves their performance, although the magnitude of the improvement depends on the particular method used.

Suggested Citation

  • Lechner, Michael & Strittmatter, Anthony, 2017. "Practical Procedures to Deal with Common Support Problems in Matching Estimation," IZA Discussion Papers 10532, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp10532
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    References listed on IDEAS

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    More about this item

    Keywords

    Empirical Monte Carlo Study; matching estimation; regression; common support; outlier; small sample performance;
    All these keywords.

    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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