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Forecasting US employment growth using forecast combining methods

  • David E. Rapach

    (Department of Economics, Saint Louis University, Saint Louis, MO 63108-3397, USA)

  • Jack K. Strauss

    (Department of Economics, Saint Louis University, Saint Louis, MO 63108-3397, USA)

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 © 2008 John Wiley & Sons, Ltd.

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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 27 (2008)
Issue (Month): 1 ()
Pages: 75-93

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Handle: RePEc:jof:jforec:v:27:y:2008:i:1:p:75-93
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