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A Test for Instrument Validity

Author

Listed:
  • Toru Kitagawa

    (Institute for Fiscal Studies and University College London)

Abstract

This paper develops a specification test for instrument validity in the heterogeneous treatment effect model with a binary treatment and a discrete instrument. The strongest testable implication for instrument validity is given by the condition for non-negativity of point- identifiable complier’s outcome densities. Our specification test infers this testable implication using a variance-weighted Kolmogorov-Smirnov test statistic. Implementation of the proposed test does not require smoothing parameters, even though the testable implications involve non-parametric densities. The test can be applied to both discrete and continuous outcome cases, and an extension of the test to settings with conditioning covariates is provided.

Suggested Citation

  • Toru Kitagawa, 2014. "A Test for Instrument Validity," CeMMAP working papers CWP34/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:34/14
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    References listed on IDEAS

    as
    1. Andrews, Donald W.K. & Shi, Xiaoxia, 2014. "Nonparametric inference based on conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 179(1), pages 31-45.
    2. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
    3. Donald W. K. Andrews & Xiaoxia Shi, 2013. "Inference Based on Conditional Moment Inequalities," Econometrica, Econometric Society, vol. 81(2), pages 609-666, March.
    4. Abadie A., 2002. "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 284-292, March.
    5. repec:cwl:cwldpp:1840rr is not listed on IDEAS
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    Cited by:

    1. Seojeong Lee, 2018. "A Consistent Variance Estimator for 2SLS When Instruments Identify Different LATEs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(3), pages 400-410, July.
    2. Kaspar Wüthrich, 2020. "A Comparison of Two Quantile Models With Endogeneity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 443-456, April.
    3. Ismael Mourifie & Yuanyuan Wan, 2015. "(Partially) Identifying potential outcome distributions in triangular systems," Working Papers tecipa-532, University of Toronto, Department of Economics.
    4. Laffers, Lukas & Mellace, Giovanni, 2015. "A Note on Testing the LATE Assumptions," Discussion Papers on Economics 4/2015, University of Southern Denmark, Department of Economics.

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