Forecasting with Dynamic Panel Data Models

Laura Liu, Hyungsik Moon () and Frank Schorfheide

No 25102, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C11 C14 C23 C53 G21 (search for similar items in EconPapers)
Date: 2018-09
New Economics Papers: this item is included in nep-for and nep-mac
Note: EFG ME
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Citations: View citations in EconPapers (4)

Published as Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Forecasting With Dynamic Panel Data Models," Econometrica, Econometric Society, vol. 88(1), pages 171-201, January.

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Related works:
Journal Article: Forecasting With Dynamic Panel Data Models (2020)
Working Paper: Forecasting with Dynamic Panel Data Models (2017)
Working Paper: Forecasting with Dynamic Panel Data Models (2016)
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