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Instrumental variables estimation of the average treatment effect in the correlated random coefficient model

In: Modelling and Evaluating Treatment Effects in Econometrics

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  • Jeffrey M. Wooldridge

Abstract

I propose a general framework for instrumental variables estimation of the average treatment effect in the correlated random coefficient model, focusing on the case where the treatment variable has some discreteness. The approach involves adding a particular function of the exogenous variables to a linear model containing interactions in observables, and then using instrumental variables for the endogenous explanatory variable. I show how the general approach applies to binary and Tobit treatment variables, including the case of multiple treatments.

Suggested Citation

  • Jeffrey M. Wooldridge, 2008. "Instrumental variables estimation of the average treatment effect in the correlated random coefficient model," Advances in Econometrics, in: Modelling and Evaluating Treatment Effects in Econometrics, pages 93-116, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-9053(07)00004-7
    DOI: 10.1016/S0731-9053(07)00004-7
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    Cited by:

    1. Michael Bates & Seolah Kim, 2024. "Estimating the price elasticity of gasoline demand in correlated random coefficient models with endogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(4), pages 679-696, June.
    2. Escanciano, Juan Carlos, 2023. "Irregular identification of structural models with nonparametric unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 234(1), pages 106-127.
    3. Gökçe, Osman Zeki & Hatipoglu, Emre & Belaïd, Fateh, 2024. "Navigating energy diplomacy in times of recovery and conflict: A study of cross-border energy trade dynamics," Resources Policy, Elsevier, vol. 90(C).
    4. Gabriel J. Power & Djerry C. Mbianda Tandja, 2022. "Should lenders also advise? Evidence from project loans," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 45(4), pages 961-985, December.
    5. Li, Baolin & Wang, Jia & Nassani, Abdelmohsen A. & Binsaeed, Rima H. & Li, Zeyun, 2023. "The future of Green energy: A panel study on the role of renewable resources in the transition to a Green economy," Energy Economics, Elsevier, vol. 127(PB).
    6. Zhu, Xun & Jin, Zequn, 2023. "Some identification results in a correlated random coefficients sample selection model," Economics Letters, Elsevier, vol. 233(C).

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