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Identification of Instrumental Variable Correlated Random Coefficients Models

Author

Listed:
  • Matthew A. Masten

    (Duke University)

  • Alexander Torgovitsky

    (Northwestern University)

Abstract

We study identification and estimation of the average partial effect in an instrumental variable correlated random coefficients model with continuously distributed endogenous regressors. This model allows treatment effects to be correlated with the level of treatment. The main result shows that the average partial effect is identified by averaging coefficients obtained from a collection of ordinary linear regressions that condition on different realizations of a control function. These control functions can be constructed from binary or discrete instruments, which may affect the endogenous variables heterogeneously. Our results suggest a simple estimator that can be implemented with a companion Stata module.

Suggested Citation

  • Matthew A. Masten & Alexander Torgovitsky, 2016. "Identification of Instrumental Variable Correlated Random Coefficients Models," The Review of Economics and Statistics, MIT Press, vol. 98(5), pages 1001-1005, December.
    • Handle: RePEc:tpr:restat:v:98:y:2016:i:5:p:1001-1005
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    Citations

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    Cited by:

    1. Bora Kim, 2020. "Analysis of Randomized Experiments with Network Interference and Noncompliance," Papers 2012.13710, arXiv.org.
    2. Francis J. DiTraglia & Camilo Garcia-Jimeno & Rossa O'Keeffe-O'Donovan & Alejandro Sanchez-Becerra, 2020. "Identifying Causal Effects in Experiments with Spillovers and Non-compliance," Papers 2011.07051, arXiv.org, revised Jan 2023.
    3. Douglas Gollin & Christopher Udry, 2021. "Heterogeneity, Measurement Error, and Misallocation: Evidence from African Agriculture," Journal of Political Economy, University of Chicago Press, vol. 129(1), pages 1-80.
    4. Samuele Centorrino & Aman Ullah & Jing Xue, 2019. "Semiparametric Estimation of Correlated Random Coefficient Models without Instrumental Variables," Papers 1911.06857, arXiv.org.
    5. Newey, Whitney & Stouli, Sami, 2021. "Control variables, discrete instruments, and identification of structural functions," Journal of Econometrics, Elsevier, vol. 222(1), pages 73-88.
    6. Paul Carrillo & Dave Donaldson & Dina Pomeranz & Monica Singhal, 2023. "Misallocation in Firm Production: A Nonparametric Analysis Using Procurement Lotteries," NBER Working Papers 31311, National Bureau of Economic Research, Inc.
    7. Louise Laage, 2020. "A Correlated Random Coefficient Panel Model with Time-Varying Endogeneity," Papers 2003.09367, arXiv.org, revised Nov 2022.
    8. Matthew A. Masten & Alexandre Poirier, 2021. "Salvaging Falsified Instrumental Variable Models," Econometrica, Econometric Society, vol. 89(3), pages 1449-1469, May.
    9. Dionissi Aliprantis & Francisca G.-C. Richter, 2020. "Evidence of Neighborhood Effects from Moving to Opportunity: Lates of Neighborhood Quality," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 633-647, October.
    10. Carolina Caetano & Gregorio Caetano & Juan Carlos Escanciano, 2023. "Regression discontinuity design with multivalued treatments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 840-856, September.
    11. DiTraglia, Francis J. & García-Jimeno, Camilo & O’Keeffe-O’Donovan, Rossa & Sánchez-Becerra, Alejandro, 2023. "Identifying causal effects in experiments with spillovers and non-compliance," Journal of Econometrics, Elsevier, vol. 235(2), pages 1589-1624.
    12. Escanciano, Juan Carlos, 2023. "Irregular identification of structural models with nonparametric unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 234(1), pages 106-127.
    13. Juan Carlos Escanciano, 2020. "Irregular Identification of Structural Models with Nonparametric Unobserved Heterogeneity," Papers 2005.08611, arXiv.org.
    14. Breen, Richard & Ermisch, John, 2021. "Instrumental Variable Estimation in Demographic Studies: The LATE interpretation of the IV estimator with heterogenous effects," SocArXiv vx9m7, Center for Open Science.

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