Inference for Forecasting Accuracy: Pooled versus Individual Estimators in High-dimensional Panel Data

Tim Kutta; Martin Schumann; Holger Dette

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arXiv · 2512.15592

Inference for Forecasting Accuracy: Pooled versus Individual Estimators in High-dimensional Panel Data

Tim Kutta·Martin Schumann·Holger Dette

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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.

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BibTeX RIS

Tim Kutta, Martin Schumann, Holger Dette. 2025-12-17. Inference for Forecasting Accuracy: Pooled versus Individual Estimators in High-dimensional Panel Data. https://arxiv.org/abs/2512.15592

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