Real-Time Forecasting With a Mixed-Frequency VAR
开发了一种混合频率（季度和月度）的向量自回归模型，采用贝叶斯方法估计，并利用实时数据评估其预测表现，发现季度内信息能改善实时预测。
This article develops a vector autoregression (VAR) for time series which are observed at mixed frequencies--quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time dataset, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time. This article has online supplementary materials.