论文标题
从预测错误中学习：一种预测组合的新方法
Learning from Forecast Errors: A New Approach to Forecast Combinations
论文作者
论文摘要
预报员经常使用常见信息，因此会犯常见错误。我们提出了一种新方法，即图形模型（FGM），以预测组合，将特质预测错误与常见错误分开。 FGM利用了预测误差的因子结构和特质误差的精确矩阵的稀疏性。我们证明了使用FGM估算的预测组合权重和均值预测误差的一致性，并通过广泛的模拟支持结果。预测宏观经济系列的经验应用表明，使用FGM的预测组合超过了使用相同权重和图形模型的预测，而无需结合预测误差的因子结构。
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.
