Gross domestic product (GDP) is the most comprehensive and authoritative measure of economic activity. The macroeconomic literature has focused on nowcasting and forecasting this measure at the monthly frequency, using related high‐frequency indicators. We address the issue of estimating monthly GDP using a large‐dimensional set of monthly indicators, by pooling the disaggregate estimates arising from simple and feasible bivariate models that consider one indicator at a time in conjunction to GDP. Our base model handles mixed‐frequency data and ragged‐edge data structure with any pattern of missingness. Our methodology enables to distil the common component of the available economic indicators, so that the monthly GDP estimates arise from the projection of the quarterly figures on the space spanned by the common component. The weights used for the combination reflect the ability to nowcast quarterly GDP and are obtained as a function of the regularized estimator of the high‐dimensional covariance matrix of the nowcasting errors. A recursive nowcasting and forecasting experiment with data on euro area GDP illustrates that the optimal weights adapt to the information set available in real time and vary according to the phase of the business cycle.

中文翻译：

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用大数据即时预测每月GDP：一种模型平均方法

国内生产总值（GDP）是最全面，最权威的经济活动指标。宏观经济学文献集中在使用相关的高频指标，以每月频率对这一措施进行临近预报和预测。我们通过汇总由简单可行的双变量模型（同时考虑一个指标和GDP一起考虑一个指标）产生的分解估算值，来解决使用大规模的月度指标集估算月度GDP的问题。我们的基本模型可处理任何缺失模式的混合频率数据和参差不齐的数据结构。我们的方法使我们能够提取可用经济指标的共同部分，以便每月GDP估算值来自对共同部分所跨越的空间的季度数字的预测。用于组合的权重反映了按季度预测GDP的能力，并且是根据临近预报误差的高维协方差矩阵的正则估计量获得的。一项关于欧元区GDP数据的递归临近预报和预测实验表明，最佳权重适应实时可用的信息集，并根据商业周期的阶段而变化。