Prediction Intervals of Panel Data Approach for Programme Evaluation
Bibliographic Data
| ID | 21653398 |
|---|---|
| Authors | Hongyi Jiang (0000-0001-9840-078X, China Center for Economic Research, Institute of Digital Finance, National School of Development Peking University Beijing China), Xingyu Li (0000-0001-5801-7989, School of Economics Zhejiang University Hangzhou Zhejiang China, corresponding author), Yan Shen (0009-0008-0461-5498, China Center for Economic Research, Institute of Digital Finance, National School of Development Peking University Beijing China), Qiankun Zhou (Department of Economics Louisiana State University Baton Rouge Louisiana USA) |
| Year | 2025 |
| Volume | 40 |
| Issue | 6 |
| Pages | 655-668 |
| Publication date | 2025-09-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Applied Econometrics (JOURNAL) |
| Journal identifiers | ISSN: 1099-1255 • E-ISSN: 0883-7252 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/jae.3134 |
| OpenAlex | W4410521581 |
| Language | EN |
| References cited | 38 |
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
Econometrics · Economics · Panel data · Statistics · Advanced Causal Inference Techniques · Agricultural risk and resilience · Computer Science · Efficiency Analysis Using DEA · Mathematics
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| Highly cited | No |
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