We incorporate external information extracted from the European Central Bank’s Survey of Professional Forecasters into the predictions of a Bayesian VAR using entropic tilting and soft conditioning. The resulting conditional forecasts significantly improve the plain BVAR point and density forecasts. Importantly, we do not restrict the forecasts at a specific quarterly horizon but their possible paths over several horizons jointly since the survey information comes in the form of one- and two-year-ahead expectations. As well as improving the accuracy of the variable that we target, the spillover effects on “other-than-targeted” variables are relevant in size and are statistically significant. We document that the baseline BVAR exhibits an upward bias for GDP growth after the financial crisis, and our results provide evidence that survey forecasts can help mitigate the effects of structural breaks on the forecasting performance of a popular macroeconometric model.

我们将使用熵倾斜和软条件从欧洲中央银行的专业预报员调查中提取的外部信息整合到贝叶斯VAR的预测中。产生的条件预测显着改善了普通BVAR点和密度预测。重要的是，我们不会将预测限制在特定的季度范围内，而是将它们在多个范围内的可能路径共同限制，因为调查信息以一年和两年的预期形式出现。除了提高我们所针对的变量的准确性外，对“非目标”变量的溢出影响在规模上也具有相关性，并且具有统计意义。我们记录到，基准BVAR在金融危机后对GDP增长呈现出向上的偏差，