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FORECASTING WITH DYNAMIC PANEL DATA MODELS
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DOI：10.3982/ECTA14952.png)
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Abstract
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This paper considers the problem of forecasting a collection of short time series using cross-sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This formula utilizes cross-sectional information to transform the unit-specific (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coefficients. We show that the risk of a predictor based on a nonparametric kernel estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlated random effects distribution as known (ratio optimality). Our empirical Bayes predictor performs well compared to various competitors in a Monte Carlo study. In an empirical application, we use the predictor to forecast revenues for a large panel of bank holding companies and compare forecasts that condition on actual and severely adverse macroeconomic conditions.
Keywords:
Bank stress tests
empirical Bayes
forecasting
panel data
ratio optimality
Tweedie's formula
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