Shakeeb Khan, Arnaud Maurel, Yichong Zhang
arXiv 3 Oct 2019 · Econometrics · 1 citations (OpenAlex)
arXiv:1910.01318 · PDF · DOI · OpenAlex · Extracted main text
We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross sectional and panel data models, and in this paper we formally quantify their identifying power in a bivariate system often employed in the treatment effects literature. Our main findings are that imposing a factor structure yields point identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models. In particular, we show that a "non-standard" exclusion restriction that requires an explanatory variable in the outcome equation to be excluded from the treatment equation is no longer necessary for identification, even in cases where all of the regressors from the outcome equation are discrete. We also establish identification of the coefficient of the endogenous regressor in models with more general factor structures, in situations where one has access to at least two continuous measurements of the common factor.
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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 | Vuong and Xu (2017) Counterfactual mapping and individual treatment effects in nonseparable models with binary endogeneity | 0.874 | 11 | 2 | 100% |
| 2 | Han and Vytlacil (2017) Identification in a generalization of bivariate probit models with endogenous regressors | 0.874 | 9 | 2 | 100% |
| 3 | Cunha, Heckman, and Schennach (2010) Estimating the Technology of Cognitive and Noncognitive Skill Formation | 0.874 | 5 | 2 | 100% |
| 4 | Carneiro, Hansen, and Heckman (2003) Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of… | 0.843 | 3 | 3 | 100% |
| 5 | Vytlacil and Yildiz (2007) Dummy Endogenous Variables in Weakly Separable Models | 0.749 | 31 | 6 | 42% |
| 6 | Hu and Schennach (2013) Nonparametric identification and semiparametric estimation of classical measurement error models without side information | 0.737 | 4 | 2 | 75% |
| 7 | Ashworth, Hotz, Maurel, and Ransom (2021) Changes Across Cohorts in Wage Returns to Schooling and Early Work Experiences | 0.737 | 3 | 2 | 100% |
| 8 | Khan and Nekipelov (2018) Information structure and statistical information in discrete response models | 0.737 | 3 | 2 | 100% |
| 9 | Shaikh and Vytlacil (2011) Partial Identification in Triangular Systems of Equations with Binary Dependent Variables | 0.737 | 3 | 2 | 100% |
| 10 | Abrevaya, Hausman, and Khan (2010) Testing for Causal Effects in a Generalized Regression Model with Endogenous Regressors self | 0.644 | 3 | 2 | 67% |
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