2020/01/01 by Laura Liu, Hyungsik Roger Moon, Frank Schorfheide · 1 voice · 3 citations
Economics, Econometrics and Finance · #Global trade and economics #Monetary Policy and Economic Impact #Spatial and Panel Data Analysis
paper · doi:10.3982/ecta14952
openalex publication_date 2020/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/03
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.