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Forecasting With Dynamic Panel Data Models

Laura Liu (), Hyungsik Moon () and Frank Schorfheide

Econometrica, 2020, vol. 88, issue 1, 171-201

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 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.

Date: 2020
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Downloads: (external link)
https://doi.org/10.3982/ECTA14952

Related works:
Working Paper: Forecasting with Dynamic Panel Data Models (2018) Downloads
Working Paper: Forecasting with Dynamic Panel Data Models (2017) Downloads
Working Paper: Forecasting with Dynamic Panel Data Models (2016) Downloads
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