Xingyu Li, Yan Shen, Qiankun Zhou
arXiv 24 Feb 2022 · Econometrics · publishedJournal of Econometrics (2024) · 5 citations (OpenAlex)
arXiv:2202.12078 · PDF · DOI · OpenAlex · Extracted main text
We consider the construction of confidence intervals for treatment effects estimated using panel models with interactive fixed effects. We first use the factor-based matrix completion technique proposed by Bai and Ng (2021) to estimate the treatment effects, and then use bootstrap method to construct confidence intervals of the treatment effects for treated units at each post-treatment period. Our construction of confidence intervals requires neither specific distributional assumptions on the error terms nor large number of post-treatment periods. We also establish the validity of the proposed bootstrap procedure that these confidence intervals have asymptotically correct coverage probabilities. Simulation studies show that these confidence intervals have satisfactory finite sample performances, and empirical applications using classical datasets yield treatment effect estimates of similar magnitudes and reliable confidence intervals.
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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 | Hsiao, C., Ching, H. S., and Wan, S. K (2012) A panel data approach for program evaluation: Measuring the benefits of political and economic integration of Hong Kong with mai… | 1.000 | 12 | 3 | 100% |
| 2 | 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 | 9 | 3 | 100% |
| 3 | Bai, J. and Ng, S (2021) Matrix completion, counterfactuals, and factor analysis of missing data | 0.941 | 18 | 7 | 83% |
| 4 | Bai, J. and Ng, S (2002) Determining the number of factors in approximate factor models | 0.928 | 4 | 4 | 100% |
| 5 | Li, K. T. and Bell, D. R (2017) Estimation of average treatment effects with panel data: Asymptotic theory and implementation | 0.928 | 4 | 3 | 100% |
| 6 | Fujiki, H. and Hsiao, C (2015) Disentangling the effects of multiple treatments—measuring the net economic impact of the 1995 great Hanshin-Awaji earthquake | 0.843 | 3 | 3 | 100% |
| 7 | Hsiao, C., Shen, Y., and Zhou, Q (2022) Multiple treatment effects in panel-heterogeneity and aggregation self | 0.843 | 3 | 3 | 100% |
| 8 | Bai, J (2009) Panel data models with interactive fixed effects | 0.794 | 10 | 5 | 50% |
| 9 | Xu, Y (2017) Generalized synthetic control method: Causal inference with interactive fixed effects models | 0.737 | 3 | 2 | 100% |
| 10 | Goncalves, S., Perron, B., and Djogbenou, A (2017) Bootstrap prediction intervals for factor models | 0.717 | 19 | 3 | 37% |
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