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Author: Kedagni, Desire
Resulting in 4 citations.
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Ban, Kyunghoon Kedagni, Desire |
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Nonparametric Bounds on Treatment Effects with Imperfect Instruments Econometrics Journal published online (29 November 2021): DOI: 10.1093/ectj/utab033. Also: https://academic.oup.com/ectj/advance-article/doi/10.1093/ectj/utab033/6445996 Cohort(s): Young Men Publisher: Royal Economic Society (RES) Keyword(s): Educational Returns; Modeling; Modeling, Nonparametric Regression; Research Methodology Permission to reprint the abstract has not been received from the publisher. This paper extends the identification results in Nevo and Rosen (2012) to nonparametric models. We derive nonparametric bounds on the average treatment effect when an imperfect instrument is available. As in Nevo and Rosen (2012), we assume that the correlation between the imperfect instrument and the unobserved latent variables has the same sign as the correlation between the endogenous variable and the latent variables. We show that the monotone treatment selection and monotone instrumental variable restrictions, introduced by Manski and Pepper (2000, 2009), jointly imply this assumption. Moreover, we show how the monotone treatment response assumption can help tighten the bounds. The identified set can be written in the form of intersection bounds, which is more conducive to inference. We illustrate our methodology using the National Longitudinal Survey of Young Men data to estimate returns to schooling. |
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Bibliography Citation
Ban, Kyunghoon and Desire Kedagni. "Nonparametric Bounds on Treatment Effects with Imperfect Instruments." Econometrics Journal published online (29 November 2021): DOI: 10.1093/ectj/utab033.
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| 2. |
Kedagni, Desire |
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Identifying Treatment Effects in the Presence of Confounded Types Journal of Econometrics published online (5 June 2021): DOI: 10.1016/j.jeconom.2021.01.012. Also:https://www.sciencedirect.com/science/article/pii/S0304407621001512 Cohort(s): Young Men Publisher: Elsevier Keyword(s): College Degree; Educational Returns; Modeling, Instrumental Variables; Statistical Analysis; Wages In this paper, I consider identification of treatment effects when the treatment is endogenous. The use of instrumental variables is a popular solution to deal with endogeneity, but this may give misleading answers when the instrument is invalid. I show that when an (unobserved) instrument is invalid due to correlation with the first stage unobserved heterogeneity, a proxy for the instrument helps partially identify not only the local average treatment effect, but also the entire potential outcomes distributions for compliers. I exploit the fact that the distribution of the observed outcome in each group defined by the treatment and the instrument is a mixture of the distributions of interest. I write the identified set in the form of conditional moment inequalities, and provide an easily implementable inference procedure. Under some tail restrictions, the potential outcomes distributions are point-identified for compliers. Finally, I illustrate my methodology on data from the National Longitudinal Survey of Young Men to estimate returns to college using college proximity as a proxy for the instrument low college cost. I find that a college degree increases the average hourly wage of the compliers by 15%-30%. |
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Bibliography Citation
Kedagni, Desire. "Identifying Treatment Effects in the Presence of Confounded Types." Journal of Econometrics published online (5 June 2021): DOI: 10.1016/j.jeconom.2021.01.012. Also:https://www.sciencedirect.com/science/article/pii/S0304407621001512.
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| 3. |
Kedagni, Desire |
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Testing Instrument Validity and Identification with Invalid Instruments Ph.D. Dissertation, Department of Economics, The Pennsylvania State University, 2018 Cohort(s): Young Men Publisher: ProQuest Dissertations & Theses (PQDT) Keyword(s): College Education; Educational Returns; Modeling, Instrumental Variables Permission to reprint the abstract has not been received from the publisher. In the second chapter, I consider identification of treatment effects when the treatment is endogenous. The use of instrumental variables is a popular solution to deal with endogeneity, but this may give misleading answers when the instrument is invalid. I show that when the instrument is invalid due to correlation with the first stage unobserved heterogeneity, a second (also possibly invalid) instrument allows to partially identify not only the local average treatment effect, but also the entire potential outcomes distributions for compliers. I exploit the fact that the distribution of the observed outcome in each group defined by the treatment and the instrument is a mixture of the distributions of interest. I write the identified set in the form of conditional moment inequalities, and provide an easily implementable inference procedure. Under some (testable) tail restrictions, the potential outcomes distributions are point-identified for compliers. Finally, I illustrate my methodology on data from the National Longitudinal Survey of Young Men to estimate returns to college using college proximity as (potential) instrument. I find that a 95% level confidence set for the average return to college for compliers is [38%, 79%]. |
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Bibliography Citation
Kedagni, Desire. Testing Instrument Validity and Identification with Invalid Instruments. Ph.D. Dissertation, Department of Economics, The Pennsylvania State University, 2018. |
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Kedagni, Desire Mourifie, Ismael |
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Generalized Instrumental Inequalities: Testing the Instrumental Variable Independence Assumption Biometrika published online (29 February 2020): DOI: 10.1093/biomet/asaa003. Also: https://academic.oup.com/biomet/advance-article/doi/10.1093/biomet/asaa003/5767137 Cohort(s): NLSY79 Publisher: Oxford University Press Keyword(s): College Cost; College Education; Modeling, Instrumental Variables; Parental Influences; Wages Permission to reprint the abstract has not been received from the publisher. This paper proposes a new set of testable implications for the instrumental variable independence assumption for discrete treatment, but unrestricted outcome and instruments: generalized instrumental inequalities. When outcome and treatment are both binary, but instruments are unrestricted, we show that the generalized instrumental inequalities are necessary and sufficient to detect all observable violations of the instrumental variable independence assumption. To test the generalized instrumental inequalities, we propose an approach combining a sample splitting procedure and an inference method for intersection bounds. This idea allows one to easily implement the test using existing Stata packages. We apply our proposed strategy to assess the validity of the instrumental variable independence assumption for various instruments used in the returns to college literature. |
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Bibliography Citation
Kedagni, Desire and Ismael Mourifie. "Generalized Instrumental Inequalities: Testing the Instrumental Variable Independence Assumption." Biometrika published online (29 February 2020): DOI: 10.1093/biomet/asaa003.
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