FORECASTING WITH DYNAMIC PANEL DATA MODELS

• 成果类型: Article
• 署名作者: Liu, Laura; Moon, Hyungsik Roger; Schorfheide, Frank
• 署名单位: Indiana University System; Indiana University Bloomington; University of Southern California; Yonsei University; University of Pennsylvania; Centre for Economic Policy Research - UK; National Bureau of Economic Research
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA14952
• 发表日期: 2020
• 页码: 171-201
• 关键词: Empirical Bayes

摘要

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 nonparametric 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.