Forecasting with panel data: Estimation uncertainty versus parameter heterogeneity

• 成果类型: Article
• 署名作者: Pesaran, M. Hashem; Pick, Andreas; Timmermann, Allan
• 署名单位: University of Cambridge; University of Southern California; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam; Tinbergen Institute; University of California System; University of California San Diego; University of California System; University of California San Diego
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE2589
• 发表日期: 2026
• 关键词: Empirical Bayes; combination; run

摘要

We provide a comprehensive examination of the predictive accuracy of panel forecasting methods based on individual, pooling, fixed effects, and empirical Bayes estimation, and propose optimal weights for forecast combination schemes. We consider linear panel data models, allowing for weakly exogenous regressors and correlated heterogeneity. We quantify the gains from exploiting panel data and demonstrate how forecasting performance depends on the degree of parameter heterogeneity, whether such heterogeneity is correlated with the regressors, the goodness-of-fit of the model, and the dimensions of the data. Monte Carlo simulations and empirical applications to house prices and CPI inflation show that empirical Bayes and forecast combination methods perform best overall and rarely produce the least accurate forecasts for individual series.