{"ID":2828122,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15592","arxiv_id":"2512.15592","title":"Inference for Forecasting Accuracy: Pooled versus Individual Estimators in High-dimensional Panel Data","abstract":"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.","short_abstract":"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, c...","url_abs":"https://arxiv.org/abs/2512.15592","url_pdf":"https://arxiv.org/pdf/2512.15592v1","authors":"[\"Tim Kutta\",\"Martin Schumann\",\"Holger Dette\"]","published":"2025-12-17T16:56:42Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"econ.EM\",\"math.ST\"]","methods":"[]","has_code":false}