Jad Beyhum, Jean-Pierre Florens, Elia Lapenta, Ingrid Van Keilegom
arXiv 10 Aug 2022 · Econometrics · publishedEconometric Reviews (2024) · 4 citations (OpenAlex)
arXiv:2208.05344 · PDF · DOI · OpenAlex · Extracted main text
The hypothesis of homogeneous treatment effects is central to the instrumental variables literature. This assumption signifies that treatment effects are constant across all subjects. It allows to interpret instrumental variable estimates as average treatment effects over the whole population of the study. When this assumption does not hold, the bias of instrumental variable estimators can be larger than that of naive estimators ignoring endogeneity. This paper develops two tests for the assumption of homogeneous treatment effects when the treatment is endogenous and an instrumental variable is available. The tests leverage a covariable that is (jointly with the error terms) independent of a coordinate of the instrument. This covariate does not need to be exogenous. The first test assumes that the potential outcomes are linear in the regressors and is computationally simple. The second test is nonparametric and relies on Tikhonov regularization. The treatment can be either discrete or continuous. We show that the tests have asymptotically correct level and asymptotic power equal to one against a range of alternatives. Simulations demonstrate that the proposed tests attain excellent finite sample performances. The methodology is also applied to the evaluation of returns to schooling and the effect of price on demand in a fish market.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | Darolles, S., Y. Fan, J.-P. Florens, and E. Renault (2011) Nonparametric instrumental regression | 0.874 | 5 | 2 | 100% |
| 2 | Angrist, J. D., G. W. Imbens, and D. B. Rubin (1996) Identification of causal effects using instrumental variables | 0.811 | 4 | 2 | 100% |
| 3 | Card, D (1995) Using geographic variation in college proximity to estimate the return to schooling | 0.644 | 4 | 1 | 100% |
| 4 | Carrasco, M., J.-P. Florens, and E. Renault (2007, January) (2007) Chapter 77 Linear Inverse Problems in Structural Econometrics Estimation Based on Spectral Decomposition and Regularization self | 0.585 | 3 | 1 | 100% |
| 5 | Centorrino, S., F. Feve, and J.-P. Florens (2017, January) (2017) Additive Nonparametric Instrumental Regressions: A Guide to Implementation | 0.585 | 3 | 1 | 100% |
| 6 | Sant’Anna, P. H (2021) Nonparametric tests for treatment effect heterogeneity with duration outcomes | 0.585 | 3 | 1 | 100% |
| 7 | Chen, X., O. Linton, and I. Van Keilegom (2003) Estimation of semiparametric models when the criterion function is not smooth | 0.511 | 2 | 1 | 100% |
| 8 | Chernozhukov, V. and C. Hansen (2005) An IV model of quantile treatment effects | 0.511 | 2 | 1 | 100% |
| 9 | Dong, Y. and S. Shen (2018) Testing for rank invariance or similarity in program evaluation | 0.511 | 2 | 1 | 100% |
| 10 | Florens, J.-P., J. Johannes, and S. V. Bellegem (2012) Instrumental regression in partially linear models self | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 38 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Average Marginal Effects in One-Step Partially Linear Instrumental Regressions | 0.000 | 1 | 1 |