Forecasting With Dynamic Panel Data Models. Issue 1 (5th February 2020)

• Record Type: Journal Article
• Authors: Liu, Laura; Moon, Hyungsik Roger; Schorfheide, Frank
• Is Part Of: Econometrica. Volume 88:Issue 1(2020)
• Page Start: 171
• Page End: 201
• Publication Date: 2020-02-05
• Subjects: Bank stress tests -- empirical Bayes -- forecasting -- panel data -- ratio optimality -- Tweedie's formula; Econometrics -- Periodicals; Economics, Mathematical -- Periodicals; Economics -- Periodicals; Économétrie -- Périodiques; Mathématiques économiques -- Périodiques; Économie politique -- Périodiques; 330.05
• DOI: 10.3982/ECTA14952
• Languages: English
• ISSNs: 0012-9682

Abstract

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.