Forecasting With Dynamic Panel Data Models
Laura Liu (),
Hyungsik Moon () and
Econometrica, 2020, vol. 88, issue 1, 171-201
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.
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Working Paper: Forecasting with Dynamic Panel Data Models (2018)
Working Paper: Forecasting with Dynamic Panel Data Models (2017)
Working Paper: Forecasting with Dynamic Panel Data Models (2016)
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