Forecasting with Dynamic Panel Data Models
Laura Liu (),
Hyungsik Moon () and
Frank Schorfheide ()
PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania
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 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.
Keywords: Bank Stress Tests; Empirical Bayes; Forecasting; Panel Data; Ratio Optimality; Tweedies Formula (search for similar items in EconPapers)
JEL-codes: C11 C14 C23 C53 G21 (search for similar items in EconPapers)
Pages: 95 pages
Date: 2016-12-21, Revised 2016-12-21
New Economics Papers: this item is included in nep-ets, nep-for and nep-mac
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Citations: View citations in EconPapers (3) Track citations by RSS feed
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Journal Article: Forecasting With Dynamic Panel Data Models (2020)
Working Paper: Forecasting with Dynamic Panel Data Models (2018)
Working Paper: Forecasting with Dynamic Panel Data Models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:16-022
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