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Using large data sets to forecast sectoral employment

机译：使用大数据集预测部门就业

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We use several models using classical and Bayesian methods to forecast employment for eight sectors of the US economy. In addition to using standard vector-autoregressive and Bayesian vector autoregressive models, we also augment these models to include the information content of 143 additional monthly series in some models. Several approaches exist for incorporating information from a large number of series. We consider two multivariate approaches-extracting common factors (principal components) and Bayesian shrinkage. After extracting the common factors, we use Bayesian factor-augmented vector autoregressive and vector error-correction models, as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive models. For an in-sample period of January 1972 to December 1989 and an out-of-sample period of January 1990 to March 2010, we compare the forecast performance of the alternative models. More specifically, we perform ex-post and ex-ante out-of-sample forecasts from January 1990 through March 2009 and from April 2009 through March 2010, respectively. We find that factor augmented models, especially error-correction versions, generally prove the best in out-of-sample forecast performance, implying that in addition to macroeconomic variables, incorporating long-run relationships along with short-run dynamics play an important role in forecasting employment. Forecast combination models, however, based on the simple average forecasts of the various models used, outperform the best performing individual models for six of the eight sectoral employment series.
机译：我们使用经典和贝叶斯方法使用几种模型来预测美国经济的八个部门的就业。除了使用标准向量自回归模型和贝叶斯向量自回归模型之外，我们还对这些模型进行了扩充，以在某些模型中包含143个附加月度序列的信息内容。存在几种用于合并来自大量序列的信息的方法。我们考虑两种多元方法-提取公因子（主要成分）和贝叶斯收缩。提取公因子后，我们使用贝叶斯因子增强的矢量自回归模型和矢量误差校正模型，以及大规模贝叶斯矢量自回归模型中的贝叶斯收缩。对于1972年1月至1989年12月的样本期和1990年1月至2010年3月的样本期，我们比较了替代模型的预测效果。更具体地说，我们分别执行从1990年1月到2009年3月以及从2009年4月到2010年3月的事后和事前样本外预测。我们发现，因子增强模型（尤其是误差校正版本）通常在样本外预测性能方面被证明是最好的，这意味着除了宏观经济变量之外，长期关系与短期动态的结合还起着重要作用。预测就业。但是，基于使用的各种模型的简单平均预测，预测组合模型的表现优于八个部门就业序列中六个模型中表现最好的单个模型。

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