Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data*
研究采用混频数据抽样方法，利用实时数据提前发现预算执行偏差，通过预测总收入和总支出的子成分来减小年末预测误差，有助于改进欧盟的财政监督。
Abstract The sovereign debt crisis has increased the importance of monitoring budgetary execution. We employ real‐time data using a mixed data sampling (MiDaS) methodology to demonstrate how budgetary slippages can be detected early on. We show that in spite of using real‐time data, the year‐end forecast errors diminish significantly when incorporating intra‐annual information. Our results show the benefits of forecasting aggregates via subcomponents, in this case total government revenue and expenditure. Our methodology could significantly improve fiscal surveillance and could therefore be an important part of the European Commission's model toolkit.