arXiv 19 Feb 2019 · Econometrics · 15 citations (OpenAlex)
arXiv:1902.07343 · PDF · DOI · OpenAlex · Extracted main text
The synthetic control method is often used in treatment effect estimation with panel data where only a few units are treated and a small number of post-treatment periods are available. Current estimation and inference procedures for synthetic control methods do not allow for the existence of spillover effects, which are plausible in many applications. In this paper, we consider estimation and inference for synthetic control methods, allowing for spillover effects. We propose estimators for both direct treatment effects and spillover effects and show they are asymptotically unbiased. In addition, we propose an inferential procedure and show it is asymptotically unbiased. Our estimation and inference procedure applies to cases with multiple treated units or periods, and where the underlying factor model is either stationary or cointegrated. In simulations, we confirm that the presence of spillovers renders current methods biased and have distorted sizes, whereas our methods yield properly sized tests and retain reasonable power. We apply our method to a classic empirical example that investigates the effect of California's tobacco control program as in Abadie et al. (2010) and find evidence of spillovers.
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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 | Andrews, D. W. K (2003) End-of-sample instability tests | 1.000 | 14 | 4 | 100% |
| 2 | Ferman, B. and Pinto, C (2021) Synthetic controls with imperfect pretreatment fit | 1.000 | 14 | 3 | 100% |
| 3 | Abadie, A., Diamond, A., and Hainmueller, J (2010) Synthetic control methods for comparative case studies: Estimating the effect of california’s tobacco control program | 1.000 | 12 | 7 | 100% |
| 4 | Li, K. T (2020) Statistical inference for average treatment effects estimated by synthetic control methods | 1.000 | 6 | 4 | 100% |
| 5 | Andrews, D. W. K. and Kim, J (2006) Tests for cointegration breakdown over a short time period | 1.000 | 5 | 3 | 100% |
| 6 | Di Stefano, R. and Mellace, G (2024) The inclusive synthetic control method | 0.874 | 5 | 2 | 100% |
| 7 | Hamilton, J. D (1994) Time series analysis | 0.644 | 4 | 1 | 100% |
| 8 | Hahn, J. and Shi, R (2017) Synthetic control and inference | 0.644 | 2 | 2 | 100% |
| 9 | Manresa, E (2013) Estimating the structure of social interactions using panel data | 0.644 | 2 | 2 | 100% |
| 10 | de Paula, A., Rasul, I., and Souza, P (2023) Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 47 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.