Working Paper: NBER ID: w25102
Authors: Laura Liu; Hyungsik Roger Moon; Frank Schorfheide
Abstract: 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 non-parametric 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: forecasting; dynamic panel data; empirical Bayes; bank revenues; macroeconomic conditions
JEL Codes: C11; C14; C23; C53; G21
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| cross-sectional information (C21) | estimation of unit-specific parameters (C51) |
| estimation of unit-specific parameters (C51) | forecast accuracy (C53) |
| plugin predictor (Y60) | forecast accuracy (C53) |
| pooled OLS predictor (C51) | forecast accuracy (C53) |
| macroeconomic conditions (E66) | bank revenues (G21) |
| empirical Bayes predictor (C11) | forecast accuracy (C53) |