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摘要翻译：
本文研究了利用面板数据中的截面信息对短时间序列进行预测的问题。在相关随机效应分布下，我们用Tweedie公式构造了非均匀系数后验均值的点预测器。该公式利用截面信息将单位特定（准）最大似然估计量转化为先验分布下的后验均值的近似，先验分布等于随机系数的总体分布。我们证明了基于Tweedie校正的非参数估计的预测器的风险与将相关随机效应分布视为已知（比率最优）的预测器的风险是渐近等价的。在蒙特卡罗研究中，我们的经验贝叶斯预测器与各种竞争对手相比表现良好。在一个实证应用中，我们使用预测器预测了一个大型银行控股公司的收入，并比较了实际和严重不利的宏观经济条件下的预测。
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英文标题：
《Forecasting with Dynamic Panel Data Models》
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作者：
Laura Liu, Hyungsik Roger Moon, Frank Schorfheide
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最新提交年份：
2017
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
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 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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PDF链接：
https://arxiv.org/pdf/1709.10193
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