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Forecasting with panel data: Estimation uncertainty versus parameter heterogeneity

Author

Listed:
  • M. Hashem Pesaran
  • Andreas Pick
  • Allan Timmermann

Abstract

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.

Suggested Citation

  • M. Hashem Pesaran & Andreas Pick & Allan Timmermann, 2026. "Forecasting with panel data: Estimation uncertainty versus parameter heterogeneity," Quantitative Economics, Econometric Society, vol. 17(2), pages 342-393, May.
    • Handle: RePEc:wly:quante:v:17:y:2026:i:2:p:342-393
    DOI: 10.3982/QE2589
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    Cited by:

    1. is not listed on IDEAS
    2. Tim Kutta & Martin Schumann & Holger Dette, 2025. "Inference for Forecasting Accuracy: Pooled versus Individual Estimators in High-dimensional Panel Data," Papers 2512.15592, arXiv.org.
    3. Mücella Şahin & Turgut Ün, 2024. "Forecasting Performance Comparison With Panel Data Models: Environmental Kuznets Curve Analysis," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul Journal of Economics-Istanbul Iktisat Dergisi, vol. 0(40), pages 208-221, June.
    4. Raffaella Giacomini & Sokbae Lee & Silvia Sarpietro, 2023. "A Robust Method for Microforecasting and Estimation of Random Effects," Working Paper Series WP 2023-26, Federal Reserve Bank of Chicago.
    5. Raffaella Giacomini & Sokbae Lee & Silvia Sarpietro, 2023. "Individual Shrinkage for Random Effects," Papers 2308.01596, arXiv.org, revised May 2026.
    6. Boyuan Zhang, 2022. "Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models," Papers 2211.16714, arXiv.org, revised Oct 2023.
    7. Juan Andres Espinosa-Torres & Jaime Ramirez-Cuellar, 2023. "The Effects of the Pandemic on Market Power and Profitability," Papers 2303.08765, arXiv.org.
    8. Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2025. "Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables," Working Papers 25-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised May 2025.
    9. Pietro Giorgio Lovaglio, 2025. "Cross‐Learning With Panel Data Modeling for Stacking and Forecast Time Series Employment in Europe," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 753-780, March.

    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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