arXiv 24 Nov 2018 · Econometrics
arXiv:1811.09837 · PDF · DOI · OpenAlex · Extracted main text
Multidimensional heterogeneity and endogeneity are important features of a wide class of econometric models. With control variables to correct for endogeneity, nonparametric identification of treatment effects requires strong support conditions. To alleviate this requirement, we consider varying coefficients specifications for the conditional expectation function of the outcome given a treatment and control variables. This function is expressed as a linear combination of either known functions of the treatment, with unknown coefficients varying with the controls, or known functions of the controls, with unknown coefficients varying with the treatment. We use this modeling approach to give necessary and sufficient conditions for identification of average treatment effects. A sufficient condition for identification is conditional nonsingularity, that the second moment matrix of the known functions given the variable in the varying coefficients is nonsingular with probability one. For known treatment functions with sufficient variation, we find that triangular models with discrete instrument cannot identify average treatment effects when the number of support points for the instrument is less than the number of coefficients. For known functions of the controls, we find that average treatment effects can be identified in general nonseparable triangular models with binary or discrete instruments. We extend our analysis to flexible models of increasing dimension and relate conditional nonsingularity to the full support condition of Imbens and Newey (2009), thereby embedding semi- and non-parametric identification into a common framework.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Masten, M., and A. Torgovitsky (2016) Identification of Instrumental Variable Correlated Random Coefficients Models | 1.000 | 8 | 4 | 100% |
| 2 | Florens, J. P., J. J. Heckman, C. Meghir, and E. Vytlacil (2008) Identification of Treatment Effects Using Control Functions in Models with Continuous, Endogenous Treatment and Heterogeneous Ef… | 1.000 | 7 | 4 | 100% |
| 3 | Chernozhukov, V., I. Fernández-Val, W. Newey, S. Stouli, and F. Vella (2020) Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models self | 1.000 | 6 | 5 | 100% |
| 4 | Imbens, G. W., and W. K. Newey (2009) Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity | 0.971 | 12 | 6 | 92% |
| 5 | Newey, W., and S. Stouli (2021) Control Variables, Discrete Instruments, and Identification of Structural Functions self | 0.950 | 7 | 4 | 86% |
| 6 | Newey, W. K., and S. Stouli (2022) Heterogeneous Coefficients, Control Variables, and Identification of Multiple Treatment Effects self | 0.874 | 6 | 4 | 67% |
| 7 | Chesher, A (2007) Instrumental Values | 0.737 | 3 | 2 | 100% |
| 8 | D'Haultfuille, X., and P. Février (2015) Identification of Nonseparable Triangular Models with Discrete Instruments | 0.737 | 3 | 2 | 100% |
| 9 | Jun, S. J (2009) Local Structural Quantile Effects in a Model with a Nonseparable Control Variable | 0.737 | 3 | 2 | 100% |
| 10 | Torgovitsky, A (2015) Identification of Nonseparable Models Using Instruments with Small Support | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 48 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions | 0.405 | 1 | 1 |