Inference for Forecasting Accuracy: Pooled versus Individual Estimators in High-dimensional Panel Data
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911324387147776 |
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| author | Kutta, Tim Schumann, Martin Dette, Holger |
| author_facet | Kutta, Tim Schumann, Martin Dette, Holger |
| contents | Panels with large time $(T)$ and cross-sectional $(N)$ dimensions are a key data structure in social sciences and other fields. A central question in panel data analysis is whether to pool data across individuals or to estimate separate models. Pooled estimators typically have lower variance but may suffer from bias, creating a fundamental trade-off for optimal estimation. We develop a new inference method to compare the forecasting performance of pooled and individual estimators. Specifically, we propose a confidence interval for the difference between their forecasting errors and establish its asymptotic validity. Our theory allows for complex temporal and cross-sectional dependence in the model errors and covers scenarios where $N$ can be much larger than $T$-including the independent case under the classical condition $N/T^2 \to 0$. The finite-sample properties of the proposed method are examined in an extensive simulation study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15592 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inference for Forecasting Accuracy: Pooled versus Individual Estimators in High-dimensional Panel Data Kutta, Tim Schumann, Martin Dette, Holger Methodology Econometrics Statistics Theory Panels with large time $(T)$ and cross-sectional $(N)$ dimensions are a key data structure in social sciences and other fields. A central question in panel data analysis is whether to pool data across individuals or to estimate separate models. Pooled estimators typically have lower variance but may suffer from bias, creating a fundamental trade-off for optimal estimation. We develop a new inference method to compare the forecasting performance of pooled and individual estimators. Specifically, we propose a confidence interval for the difference between their forecasting errors and establish its asymptotic validity. Our theory allows for complex temporal and cross-sectional dependence in the model errors and covers scenarios where $N$ can be much larger than $T$-including the independent case under the classical condition $N/T^2 \to 0$. The finite-sample properties of the proposed method are examined in an extensive simulation study. |
| title | Inference for Forecasting Accuracy: Pooled versus Individual Estimators in High-dimensional Panel Data |
| topic | Methodology Econometrics Statistics Theory |
| url | https://arxiv.org/abs/2512.15592 |