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Testing Local Average Treatment Effect Assumptions

Author

Listed:
  • Ismael Mourifié

    (University of Toronto)

  • Yuanyuan Wan

    (University of Toronto)

Abstract

In this paper, we propose an easy-to-implement procedure to test the key conditions for the identification and estimation of the local average treatment effect (LATE; Imbens & Angrist, 1994). We reformulate the testable implications of LATE assumptions as two conditional inequalities, which can be tested in the intersection bounds framework of Chernozhukov, Lee, and Rosen (2013) and easily implemented using the Stata package of Chernozhukov et al. (2015). We apply the proposed tests to the draft eligibility instrument in Angrist (1991), the college proximity instrument in Card (1993), and the same-sex instrument in Angrist and Evans (1998).

Suggested Citation

  • Ismael Mourifié & Yuanyuan Wan, 2017. "Testing Local Average Treatment Effect Assumptions," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 305-313, May.
    • Handle: RePEc:tpr:restat:v:99:y:2017:i:2:p:305-313
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    References listed on IDEAS

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    1. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    2. Victor Chernozhukov & Wooyoung Kim & Sokbae Lee & Adam M. Rosen, 2015. "Implementing intersection bounds in Stata," Stata Journal, StataCorp LP, vol. 15(1), pages 21-44, March.
    3. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
    4. Kasy, Maximilian, "undated". "Instrumental variables with unrestricted heterogeneity and continuous treatment - DON'T CITE! SEE ERRATUM BELOW," Working Paper 33257, Harvard University OpenScholar.
    5. David Card, 1993. "Using Geographic Variation in College Proximity to Estimate the Return to Schooling," Working Papers 696, Princeton University, Department of Economics, Industrial Relations Section..
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    12. David Card, 1993. "Using Geographic Variation in College Proximity to Estimate the Return to Schooling," Working Papers 696, Princeton University, Department of Economics, Industrial Relations Section..
    13. Marc Henry & Ismael Mourifié, 2012. "Sharp Bounds in the Binary Roy Model," CIRJE F-Series CIRJE-F-835, CIRJE, Faculty of Economics, University of Tokyo.
    14. Angrist, Joshua D & Evans, William N, 1998. "Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size," American Economic Review, American Economic Association, vol. 88(3), pages 450-477, June.
    15. Martin Huber & Giovanni Mellace, 2015. "Testing Instrument Validity for LATE Identification Based on Inequality Moment Constraints," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 398-411, May.
    16. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-209, January.
    17. Bhattacharya, Jay & Shaikh, Azeem M. & Vytlacil, Edward, 2012. "Treatment effect bounds: An application to Swan–Ganz catheterization," Journal of Econometrics, Elsevier, vol. 168(2), pages 223-243.
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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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