Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset
Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset
复制标题
DOI：
10.1198/jbes.2009.07214
复制
发表时间：
2009-10-01
影响因子：
3
通讯作者：
Wright, Jonathan H.
中科院分区：
文献类型：
--
作者：
Faust, Jon;Wright, Jonathan H.
最近的许多文章发现，使用许多预测因子的非理论预测方法比各种小模型方法对关键宏观经济变量的预测更好。然而，这些结果的实际相关性值得怀疑，因为这些文章通常使用预测者无法获得的事后修订数据，而且没有与最佳实际做法进行比较。我们使用提供了有关这两点的一些证据。一个新的大型数据集，与美联储的绿皮书预测同步。该数据集包含自1979年以来每次绿皮书预测时观察到的大量变量。我们比较实时，大数据集预测简单的单变量方法和绿皮书预测。对于通货膨胀，我们发现，单变量的方法是占主导地位的最好的非理论的大数据集的方法，这些，反过来，占主导地位的绿皮书。相比之下，对于GDP增长，我们发现，一旦考虑到绿皮书在评估当前经济状况方面的优势，大型数据集方法和绿皮书过程都没有比单变量自回归预测更大的优势。
Many recent articles have found that atheoretical forecasting methods using many predictors give better predictions for key macroeconomic variables than various small-model methods. The practical relevance of these results is open to question, however, because these articles generally use ex post revised data not available to forecasters and because no comparison is made to best actual practice. We provide some evidence on both of these points using. a new large dataset of vintage data synchronized with the Fed's Greenbook forecast. This dataset consist of a large number of variables as observed at the time of each Greenbook forecast since 1979. We compare realtime, large dataset predictions to both simple univariate methods and to the Greenbook forecast. For inflation we find that univariate methods are dominated by the best atheoretical large dataset methods and that these, in turn, are dominated by Greenbook. For GDP growth, in contrast, we find that once one takes account of Greenbook's advantage in evaluating the current state of the economy, neither large dataset methods, nor the Greenbook process offers much advantage over it univariate autoregressive forecast.