Forecast Pooling for European Macroeconomic Variables*
比较了多种预测池化方法及58个线性、时变和非线性模型的预测效果，基于约500个欧洲货币联盟国家宏观经济变量的数据集。研究发现组合方法整体表现良好，但非线性模型在某些序列上更优。
Abstract We compare alternative forecast pooling methods and 58 forecasts from linear, time‐varying and non‐linear models, using a very large dataset of about 500 macroeconomic variables for the countries in the European Monetary Union. On average, combination methods work well but single non‐linear models can outperform them for several series. The performance of pooled forecasts, and of non‐linear models, improves when focusing on a subset of unstable series, but the gains are minor. Finally, on average over the EMU countries, the pooled forecasts behave well for industrial production growth, unemployment and inflation, but they are often beaten by non‐linear models for each country and variable.