Forecasting with Dynamic Panel Data Models

Laura Liu, Hyungsik Moon () and Frank Schorfheide

Papers from arXiv.org

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 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.

Date: 2017-09
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-mac
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Citations: View citations in EconPapers (5)

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http://arxiv.org/pdf/1709.10193 Latest version (application/pdf)

Related works:
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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