We assess the forecasting performance of the nowcasting model developed at the New York FED. We show that the observation regarding a striking difference in the model’s predictive ability across business cycle phases made earlier in the literature also applies here. During expansions, the nowcasting model forecasts at best are at least as good as the historical mean model, whereas during the recessionary periods, there are very substantial gains corresponding in the reduction in MSFE of about 90% relative to the benchmark model. We show how the asymmetry in the relative forecasting performance can be verified by the use of such recursive measures of relative forecast accuracy as Cumulated Sum of Squared Forecast Error Difference (CSSFED) and Recursive Relative Mean Squared Forecast Error (based on Rearranged observations) (R²MSFE(+R)). Ignoring these asymmetries results in a biased judgement of the relative forecasting performance of the competing models over a sample as a whole, as well as during economic expansions, when the forecasting accuracy of a more sophisticated model relative to naive benchmark models tends to be overstated. Hence, care needs to be exercised when ranking several models by their forecasting performance without taking into consideration various states of the economy.

中文翻译：

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纽约联储工作人员的近况与现实：我们可以从中了解到未来，现在和过去吗？§

我们评估了纽约联储局开发的临近预报模型的预测性能。我们显示，文献早些时候关于跨整个商业周期阶段模型的预测能力显着差异的观察结果也适用于此。在扩张过程中，临近预报模型的预测充其量至少与历史均值模型一样好，而在经济衰退期，相对于基准模型，MSFE下降了约90％，有相当可观的收益。我们展示了如何通过使用相对预测准确性的递归度量来验证相对预测性能的不对称性，例如相对于预测误差平方和的累积总和（基于重新排列的观测值）的递归相对均方预测误差（R ² MSFE（+ R））。忽略这些不对称性会导致对竞争模型相对于整个样本以及经济扩张期间相对预测性能的判断有偏差，因为相对于天真的基准模型而言，更复杂的模型的预测准确性往往被夸大了。因此，在根据模型的预测性能对几种模型进行排名时，必须谨慎行事，而无需考虑各种经济状况。