Christophe Bruneel-Zupanc, Jad Beyhum
arXiv 8 Jan 2024 · Econometrics
arXiv:2401.03990 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a novel identification strategy relying on quasi-instrumental variables (quasi-IVs). A quasi-IV is a relevant but possibly invalid IV because it is not exogenous or not excluded. We show that a variety of models with discrete or continuous endogenous treatment which are usually identified with an IV - quantile models with rank invariance, additive models with homogenous treatment effects, and local average treatment effect models - can be identified under the joint relevance of two complementary quasi-IVs instead. To achieve identification, we complement one excluded but possibly endogenous quasi-IV (e.g., "relevant proxies" such as lagged treatment choice) with one exogenous (conditional on the excluded quasi-IV) but possibly included quasi-IV (e.g., random assignment or exogenous market shocks). Our approach also holds if any of the two quasi-IVs turns out to be a valid IV. In practice, being able to address endogeneity with complementary quasi-IVs instead of IVs is convenient since there are many applications where quasi-IVs are more readily available. Difference-in-differences is a notable example: time is an exogenous quasi-IV while the group assignment acts as a complementary excluded quasi-IV.
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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 | De Chaisemartin, C. and d’Haultfoeuille, X (2018) Fuzzy differences-in-differences | 1.000 | 6 | 3 | 100% |
| 2 | Chernozhukov, V. and Hansen, C (2005) An IV model of quantile treatment effects | 0.874 | 6 | 2 | 100% |
| 3 | Imbens, G. W. and Angrist, J. D (1994) Identification and estimation of local average treatment effects | 0.874 | 6 | 2 | 100% |
| 4 | Bruneel-Zupanc, C (2023) Don't (fully) exclude me, it's not necessary! identification with semi-IVs self | 0.843 | 3 | 3 | 100% |
| 5 | Angrist, J., Bettinger, E., Bloom, E., King, E., and Kremer, M (2002) Vouchers for private schooling in colombia: Evidence from a randomized natural experiment | 0.737 | 3 | 2 | 100% |
| 6 | Athey, S. and Imbens, G. W (2006) Identification and inference in nonlinear difference-in-differences models | 0.737 | 3 | 2 | 100% |
| 7 | Heckman, J. J. and Vytlacil, E (2005) Structural equations, treatment effects, and econometric policy evaluation | 0.737 | 3 | 2 | 100% |
| 8 | Caetano, C. and Escanciano, J. C (2021) Identifying multiple marginal effects with a single instrument | 0.693 | 5 | 1 | 100% |
| 9 | D’Haultfœuille, X., Hoderlein, S., and Sasaki, Y (2024) Testing and relaxing the exclusion restriction in the control function approach | 0.644 | 2 | 2 | 100% |
| 10 | Abadie, A., Angrist, J., and Imbens, G (2002) Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 73 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 1820 Don't (fully) exclude me, it's not necessary! Causal inference with semi-IVs | 0.000 | 1 | 1 |
| 2 | 1420 Dynamic Discrete-Continuous Choice Models: Identification and Conditional Choice Probability Estimation | 0.000 | 1 | 1 |