Macroeconomic Forecasting With Mixed-Frequency Data
研究混合数据频率抽样（MIDAS）方法能否利用月度数据改进美国实际产出增长的季度预测，发现加入当前季度月度数据能显著提升预测精度。
Many macroeconomic series, such as U.S. real output growth, are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS specification used in the comparison uses a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to significant improvement in forecasting current and next quarter output growth, and that MIDAS is an effective way to exploit monthly data compared with alternative methods.