Forecasting with Bayesian Vector Autoregressions Estimated Using Professional Forecasts
利用专业预测估计贝叶斯向量自回归并开展预测
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…