  We provide a comprehensive examination of the predictive accuracy of panel
forecasting methods based on individual, pooling, fixed effects, and Bayesian
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 cross-sectional ($N$) and time ($T$)
dimensions. Monte Carlo simulations and empirical applications to house prices
and CPI inflation show that forecast combination and Bayesian forecasting
methods perform best overall and rarely produce the least accurate forecasts
for individual series.
