arXiv 16 Aug 2019 · Econometrics · publishedJournal of Econometrics (2021) · 5 citations (OpenAlex)
arXiv:1908.05894 · PDF · DOI · OpenAlex · Extracted main text
Policy evaluation is central to economic data analysis, but economists mostly work with observational data in view of limited opportunities to carry out controlled experiments. In the potential outcome framework, the panel data approach (Hsiao, Ching and Wan, 2012) constructs the counterfactual by exploiting the correlation between cross-sectional units in panel data. The choice of cross-sectional control units, a key step in its implementation, is nevertheless unresolved in data-rich environment when many possible controls are at the researcher's disposal. We propose the forward selection method to choose control units, and establish validity of the post-selection inference. Our asymptotic framework allows the number of possible controls to grow much faster than the time dimension. The easy-to-implement algorithms and their theoretical guarantee extend the panel data approach to big data settings.
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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 | Carvalho, C., Masini, R., Medeiros, M.C (2018) Arco: an artificial counterfactual approach for high-dimensional panel time-series data | 1.000 | 5 | 3 | 100% |
| 2 | Das, A., Kempe, D (2011) Submodular meets spectral: greedy algorithms for subset selection, sparse approximation and dictionary selection | 0.874 | 6 | 3 | 67% |
| 3 | Belloni, A., Chernozhukov, V., Kato, K (2014) Uniform post-selection inference for least absolute deviation regression and other z-estimation problems | 0.737 | 3 | 2 | 100% |
| 4 | Li, K.T., Bell, D.R (2017) Estimation of average treatment effects with panel data: Asymptotic theory and implementation | 0.737 | 3 | 2 | 100% |
| 5 | Sunklodas, J (2000) Approximation of distributions of sums of weakly dependent random variables by the normal distribution | 0.644 | 3 | 2 | 67% |
| 6 | Belloni, A., Chernozhukov, V., Fernández-Val, I., Hansen, C (2017) Program evaluation and causal inference with high-dimensional data | 0.644 | 2 | 2 | 100% |
| 7 | Berk, R., Brown, L., Buja, A., Zhang, K., Zhao, L (2013) Valid post-selection inference | 0.644 | 2 | 2 | 100% |
| 8 | Bickel, P., Ritov, Y., Tsybakov, A (2009) Simultaneous analysis of Lasso and Dantzig selector | 0.644 | 2 | 2 | 100% |
| 9 | Bühlmann, P (2006) Boosting for high-dimensional linear models | 0.644 | 2 | 2 | 100% |
| 10 | Bühlmann, P., van de Geer, S (2011) Statistics for high-dimensional data: methods, theory and applications | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 70 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Bayesian Synthetic Control with a Soft Simplex Constraint | 1.000 | 8 | 3 |
| 2 | Splash! Robustifying Donor Pools for Policy Studies | 0.874 | 5 | 2 |
| 3 | A Relaxation Approach to Synthetic Control | 0.644 | 2 | 2 |
| 4 | On LASSO for High Dimensional Predictive Regression | 0.405 | 1 | 1 |
| 5 | A Synthetic Business Cycle Approach to Counterfactual Analysis with Nonstationary Macroeconomic Data | 0.405 | 1 | 1 |