Sayoni Roychowdhury, Indrila Ganguly, Abhik Ghosh
arXiv 25 Dec 2021 · Statistics — Methodology · publishedStatistical Papers (2023)
arXiv:2112.13228 · PDF · DOI · OpenAlex · Extracted main text
In order to evaluate the impact of a policy intervention on a group of units over time, it is important to correctly estimate the average treatment effect (ATE) measure. Due to lack of robustness of the existing procedures of estimating ATE from panel data, in this paper, we introduce a robust estimator of the ATE and the subsequent inference procedures using the popular approach of minimum density power divergence inference. Asymptotic properties of the proposed ATE estimator are derived and used to construct robust test statistics for testing parametric hypotheses related to the ATE. Besides asymptotic analyses of efficiency and powers, extensive simulation studies are conducted to study the finite-sample performances of our proposed estimation and testing procedures under both pure and contaminated data. The robustness of the ATE estimator is further investigated theoretically through the influence functions analyses. Finally our proposal is applied to study the long-term economic effects of the 2004 Indian Ocean earthquake and tsunami on the (per-capita) gross domestic products (GDP) of five mostly affected countries, namely Indonesia, Sri Lanka, Thailand, India and Maldives.
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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, S.H., and Wan, K.S (2012) A panel data approach for program evaluation: measuring the benefits of political and economic integration of Hong kong with mai… | 1.000 | 9 | 3 | 100% |
| 2 | Li, K. T., and Bell, D. R (2017) Estimation of average treatment effects with panel data: Asymptotic theory and implementation | 1.000 | 9 | 3 | 100% |
| 3 | Basu, A., Shioya, H. and Park, C (2011) Statistical Inference: The Minimum Distance Approach | 0.843 | 3 | 3 | 100% |
| 4 | Ghosh, A., and Basu, A (2013) Robust estimation for independent non-homogeneous observations using density power divergence with applications to linear regres… self | 0.794 | 10 | 5 | 50% |
| 5 | Li, K. T., and Bell, D. R (2016) Augmented Difference-in-Differences: Estimation and Inference of Average Treatment Effects | 0.737 | 3 | 2 | 100% |
| 6 | Ghosh, A. and Basu, A (2015) Robust Estimation for Non-Homogeneous Data and the Selection of the Optimal Tuning Parameter: The DPD Approach self | 0.644 | 2 | 2 | 100% |
| 7 | Basu, A., Harris, I. R., Hjort, N. L., and Jones, M. C (1998) Robust and efficient estimation by minimising a density power divergence | 0.511 | 3 | 2 | 33% |
| 8 | Ghosh, A., and Basu, A (2016) Robust Estimation in Generalized Linear Models : The Density Power Divergence Approach self | 0.511 | 2 | 2 | 50% |
| 9 | Durio, A., and Isaia, E. D (2011) The minimum density power divergence approach in building robust regression models | 0.511 | 2 | 1 | 100% |
| 10 | Abadie, A., and Gardeazabal, J (2003) The economic costs of conflict: A case study of the Basque Country | 0.405 | 1 | 1 | 100% |
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