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Prediction Intervals of Panel Data Approach for Programme Evaluation

Author

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  • Hongyi Jiang
  • Xingyu Li
  • Yan Shen
  • Qiankun Zhou

Abstract

We consider the inference on individual and time specific treatment effects on the treated within the framework of panel data approach for programme evaluation. We formulate the target problem as constructing prediction intervals for high‐dimensional linear regressions with weakly dependent data. Post‐LASSO OLS is used for estimation, while dependent wild bootstrap and simple residual bootstrap are used for the construction of prediction intervals. The proposed prediction intervals are proved to have asymptotic validity as the number of pretreatment times goes to infinity. In the proof, we also establish the model selection consistency of LASSO for dependent data and under bootstrap measure, which may be of independent interest. Monte Carlo experiments illustrate that our method outperforms existing methods in finite samples under a wide variety of data generating processes except nonstationary data. Two empirical applications are also provided.

Suggested Citation

  • Hongyi Jiang & Xingyu Li & Yan Shen & Qiankun Zhou, 2025. "Prediction Intervals of Panel Data Approach for Programme Evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(6), pages 655-668, September.
    • Handle: RePEc:wly:japmet:v:40:y:2025:i:6:p:655-668
    DOI: 10.1002/jae.3134
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    References listed on IDEAS

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    1. Kathleen T. Li, 2020. "Statistical Inference for Average Treatment Effects Estimated by Synthetic Control Methods," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 2068-2083, December.
    2. Ricardo Masini & Marcelo C. Medeiros, 2021. "Counterfactual Analysis With Artificial Controls: Inference, High Dimensions, and Nonstationarity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1773-1788, October.
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