Forecasting with panel data: estimation uncertainty versus parameter heterogeneity

Mohammad Pesaran, Andreas Pick and Allan Timmermann

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: We provide a comprehensive examination of the predictive performance 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.

Keywords: Forecasting, Panel data, Heterogeneity, Pooled estimation; Forecast combination (search for similar items in EconPapers)
JEL-codes: C33 C53 (search for similar items in EconPapers)
Date: 2022-03-21
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-ore and nep-ure
Note: mhp1
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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https://www.econ.cam.ac.uk/sites/default/files/pub ... pe-pdfs/cwpe2219.pdf

Related works:
Working Paper: Forecasting with panel data: Estimation uncertainty versus parameter heterogeneity (2025)
Working Paper: Forecasting With Panel Data: Estimation Uncertainty Versus Parameter Heterogeneity (2022)
Working Paper: Forecasting with panel data: estimation uncertainty versus parameter heterogeneity (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:2219

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