Forecasting with Dynamic Pane Data Models
95 Pages Posted: 31 Jan 2017
Date Written: December 21, 2016
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 coeffcients under a correlated random effects distribution. This formula utilizes cross-sectional information to transform the unit-speciffc (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coeffcients. We show that the risk of a predictor based on a non-parametric 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, Tweedies Formula
JEL Classification: C11, C14, C23, C53, G21
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