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
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)
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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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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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