Nowcasting Monthly GDP with Big Data: A Model Averaging Approach
本文提出一种模型平均方法，利用大量月度指标估计月度GDP，通过组合简单双变量模型的估计结果，并基于正则化协方差矩阵确定最优权重，以欧元区数据验证了该方法在实时信息下的有效性。
Abstract 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.