Forecasting with Bayesian Vector Autoregressions Estimated Using Professional Forecasts
提出一种贝叶斯收缩方法，将短期调查预测作为额外信息引入向量自回归模型，通过将变量与其调查即时预测联合建模，在宏观数据上比多种基准方法获得更小的均方预测误差。
Summary We propose a Bayesian shrinkage approach for vector autoregressions (VARs) that uses short‐term survey forecasts as an additional source of information about model parameters. In particular, we augment the vector of dependent variables by their survey nowcasts, and claim that each variable modelled in the VAR and its nowcast are likely to depend in a similar way on the lagged dependent variables. In an application to macroeconomic data, we find that the forecasts obtained from a VAR fitted by our new shrinkage approach typically yield smaller mean squared forecast errors than the forecasts obtained from a range of benchmark methods. Copyright © 2015 John Wiley & Sons, Ltd.