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Forecasting US employment growth using forecast combining methods

机译：使用预测合并方法预测美国就业增长

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摘要

We examine different approaches to forecasting monthly US employment growth in the presence of many potentially relevant predictors. We first generate simulated out-of-sample forecasts of US employment growth at multiple horizons using individual autoregressive distributed lag (ARDL) models based on 30 potential predictors. We then consider different methods from the extant literature for combining the forecasts generated by the individual ARDL models. Using the mean square forecast error (MSFE) metric, we investigate the performance of the forecast combining methods over the last decade, as well as five periods centered on the last five US recessions. Overall, our results show that a number of combining methods outperform a benchmark autoregressive model. Combining methods based on principal components exhibit the best overall performance, while methods based on simple averaging, clusters, and discount MSFE also perform well. On a cautionary note, some combining methods, such as those based on ordinary least squares, often perform quite poorly. Copyright (c) 2008 John Wiley & Sons, Ltd.
机译：在存在许多潜在的相关预测因素的情况下，我们研究了预测美国每月就业增长的不同方法。我们首先使用基于30个潜在预测变量的个体自回归分布滞后（ARDL）模型，生成多水平美国就业增长的模拟样本外预测。然后，我们考虑与现有文献不同的方法，以结合各个ARDL模型生成的预测。使用均方预测误差（MSFE）指标，我们调查了过去十年以及以美国最近五次衰退为中心的五个时期的预测组合方法的效果。总体而言，我们的结果表明，许多组合方法的性能均优于基准自回归模型。基于主成分的组合方法表现出最佳的整体性能，而基于简单平均，聚类和折扣MSFE的方法也表现良好。请注意，某些合并方法（例如基于普通最小二乘法的合并方法）通常效果不佳。版权所有（c）2008 John Wiley＆Sons，Ltd.

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