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Conditional Moment Restrictions and Triangular Simultaneous Equations

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  • Jinyong Hahn

    (University of California, Los Angeles)

  • Geert Ridder

    (University of Southern California)

Abstract

It is shown that in a nonparametric nonseparable triangular system, the conditional moment restriction (CMR) does not identify the average structural function (ASF). The CMR identifies the ASF only if the model is structurally separable in observable covariates and unobservable random errors. This excludes, for instance, random coefficient models in which the CMR in general does not identify the average response. An implication of our results is that empirical researchers should use methods other than CMR if they want to estimate the average response in models that are not additively separable. © 2011 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • Jinyong Hahn & Geert Ridder, 2011. "Conditional Moment Restrictions and Triangular Simultaneous Equations," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 683-689, May.
    • Handle: RePEc:tpr:restat:v:93:y:2011:i:2:p:683-689
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    Cited by:

    1. Jinhyun Lee, 2013. "A Consistent Nonparametric Bootstrap Test of Exogeneity," Discussion Paper Series, School of Economics and Finance 201316, School of Economics and Finance, University of St Andrews.
    2. Pereda-Fernández, Santiago, 2023. "Identification and estimation of triangular models with a binary treatment," Journal of Econometrics, Elsevier, vol. 234(2), pages 585-623.
    3. Kasy, Maximilian, "undated". "Instrumental variables with unrestricted heterogeneity and continuous treatment - DON'T CITE! SEE ERRATUM BELOW," Working Paper 33257, Harvard University OpenScholar.
    4. Lee, Jinhyun, 2013. "A Consistent Nonparametric Bootstrap Test of Exogeneity," SIRE Discussion Papers 2013-95, Scottish Institute for Research in Economics (SIRE).
    5. Halbert White & Karim Chalak, 2013. "Identification and Identification Failure for Treatment Effects Using Structural Systems," Econometric Reviews, Taylor & Francis Journals, vol. 32(3), pages 273-317, November.
    6. Pengzhou Wu & Kenji Fukumizu, 2021. "Towards Principled Causal Effect Estimation by Deep Identifiable Models," Papers 2109.15062, arXiv.org, revised Nov 2021.
    7. Hahn, Jinyong & Ridder, Geert, 2017. "Instrumental variable estimation of nonlinear models with nonclassical measurement error using control variables," Journal of Econometrics, Elsevier, vol. 200(2), pages 238-250.
    8. Carlson, Alyssa, 2023. "Relaxing conditional independence in an endogenous binary response model," Journal of Econometrics, Elsevier, vol. 232(2), pages 490-500.
    9. Schennach, Susanne & White, Halbert & Chalak, Karim, 2012. "Local indirect least squares and average marginal effects in nonseparable structural systems," Journal of Econometrics, Elsevier, vol. 166(2), pages 282-302.
    10. Hahn, Jinyong & Ridder, Geert, 2019. "Three-stage semi-parametric inference: Control variables and differentiability," Journal of Econometrics, Elsevier, vol. 211(1), pages 262-293.
    11. Bora Kim, 2020. "Analysis of Randomized Experiments with Network Interference and Noncompliance," Papers 2012.13710, arXiv.org.
    12. Lee, Jinhyun, 2013. "Sharp Bounds on Heterogeneous Individual Treatment Responses," SIRE Discussion Papers 2013-89, Scottish Institute for Research in Economics (SIRE).
    13. Maximilian Kasy, 2014. "Instrumental Variables with Unrestricted Heterogeneity and Continuous Treatment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(4), pages 1614-1636.
    14. Lu, Xun & White, Halbert, 2014. "Testing for separability in structural equations," Journal of Econometrics, Elsevier, vol. 182(1), pages 14-26.
    15. Jinhyun Lee, 2013. "Sharp Bounds on Heterogeneous Individual Treatment Responses," Discussion Paper Series, School of Economics and Finance 201310, School of Economics and Finance, University of St Andrews.

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