Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty
研究了在宏观经济数据存在修正的情况下，如何用贝叶斯向量自回归模型进行密度预测，提出了两种考虑数据不确定性的方法，其中联合估计数据修正模型的方法优于传统做法，尤其对美国数据效果明显。
Summary Macroeconomic data are subject to data revisions. Yet, the usual way of generating real‐time density forecasts from Bayesian Vector Autoregressive (BVAR) models makes no allowance for data uncertainty from future data revisions. We develop methods of allowing for data uncertainty when forecasting with BVAR models with stochastic volatility. First, the BVAR forecasting model is estimated on real‐time vintages. Second, the BVAR model is jointly estimated with a model of data revisions such that forecasts are conditioned on estimates of the ‘true’ values. We find that this second method generally improves upon conventional practice for density forecasting, especially for the United States.