摘要翻译：
预报员经常使用常见的信息，因此会犯常见的错误。我们提出了一种新的方法，因子图形模型(FGM)来预测组合，将特殊的预测误差与常见的预测误差分开。FGM充分利用了预测误差的因子结构和特征误差的精度矩阵的稀疏性。我们证明了FGM估计的预测组合权值与均方预测误差的一致性，并通过大量仿真验证了结果的正确性。在宏观经济序列预测中的实证应用表明，在不考虑预测误差因素结构的情况下，使用FGM的预测组合优于使用等权重和图形模型的组合预测。
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英文标题：
《Learning from Forecast Errors: A New Approach to Forecast Combinations》
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作者：
Tae-Hwy Lee and Ekaterina Seregina
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最新提交年份：
2021
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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：
Forecasters often use common information and hence make common mistakes. We propose a new approach, Factor Graphical Model (FGM), to forecast combinations that separates idiosyncratic forecast errors from the common errors. FGM exploits the factor structure of forecast errors and the sparsity of the precision matrix of the idiosyncratic errors. We prove the consistency of forecast combination weights and mean squared forecast error estimated using FGM, supporting the results with extensive simulations. Empirical applications to forecasting macroeconomic series shows that forecast combination using FGM outperforms combined forecasts using equal weights and graphical models without incorporating factor structure of forecast errors.
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