Forecasting with Dynamic Panel Data Models
Laura Liu (),
Hyungsik Moon () and
Frank Schorfheide ()
Papers from arXiv.org
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.
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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 (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1709.10193
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