arXiv 12 Mar 2024 · Econometrics · publishedJournal of Business and Economic Statistics (2024) · 4 citations (OpenAlex)
arXiv:2403.08130 · PDF · DOI · OpenAlex · Extracted main text
A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captured by the model. While the literature has focused on sampling uncertainty, it vanishes with the sample size. Often overlooked is the possibility that the out-of-sample error can be informative about the missing counterfactual outcome if it is mutually or serially correlated. Motivated by the best linear unbiased predictor (\blup) of \citet{goldberger:62} in a time series setting, we propose an improved predictor of potential outcome when the errors are correlated. The proposed \pup\; is practical as it is not restricted to linear models, can be used with consistent estimators already developed, and improves mean-squared error for a large class of strong mixing error processes. Ignoring predictability in the errors can distort conditional inference. However, the precise impact will depend on the choice of estimator as well as the realized values of the residuals.
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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 | Goldberger (1962) Best Linear Unbiased Prediction in the Generalized Linear Regression Model | 1.000 | 5 | 4 | 100% |
| 2 | Fan, Masini, and Medeiros (2022) Do We Exploit All Information for Counterfactual Analysis? Benefits of Factor Models and Idiosyncractic Correction | 0.843 | 3 | 3 | 100% |
| 3 | Bai and Ng (2021) Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data | 0.811 | 4 | 2 | 100% |
| 4 | Chernozhukov, Wüthrich, and Zhu (2021) An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls | 0.737 | 3 | 2 | 100% |
| 5 | Ferman and Pinto (2021) Synthetic Controls with Imperfect Pretreatment Fit | 0.511 | 2 | 1 | 100% |
| 6 | Arkhangelsky, Athey, Hirshberg, Imbens, and Wager (2021) Syntheticx Difference-in-Differences | 0.511 | 2 | 1 | 100% |
| 7 | Abadie, Diamond, and Hainmueller (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program | 0.405 | 1 | 1 | 100% |
| 8 | Abadie and Gardeazabal (2003) The Economic Costs of Conflict: A Case Study of the Basque Country | 0.405 | 1 | 1 | 100% |
| 9 | Athey, Bayati, Doudchenko, Imbens, and Khosravi (2021) Matrix Completion Methods for Causal Panel Data Models | 0.405 | 1 | 1 | 100% |
| 10 | Baltagi (2013) Panel Data Forecasting | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 31 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inference with few treated units | 0.874 | 5 | 2 |
| 2 | Difference-in-differences with as few as two cross-sectional units – A new perspective to the democracy–growth debate | 0.000 | 2 | 2 |