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Forecasting with panel data: Estimation uncertainty versus parameter heterogeneity
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PRE
AI
DOI：10.3982/qe2589.png)
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Abstract
En 中文
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.
Keywords:
Forecasting
panel data
heterogeneity
pooled estimation
empirical Bayes
forecast combination
C33
C53
Journal
Q
IF:
2.2
Papers:
24
Citations:
0
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