In this paper, we address whether using a disaggregated series or combining an aggregated and disaggregated series improves the forecasting of the aggregated series compared to using the aggregated series alone. We used econometric techniques, such as the weighted lag adaptive least absolute shrinkage and selection operator, and Exponential Triple Smoothing (ETS), as well as the Autometrics algorithm to forecast industrial production in Brazil one to twelve months ahead. This is the novelty of the work, as is the use of the average multi-horizon Superior Predictive Ability (aSPA) and uniform multi-horizon Superior Predictive Ability (uSPA) tests, used to select the best forecasting model by combining different horizons. Our sample covers the period from January 2002 to February 2020. The disaggregated ETS has a better forecast performance when forecasting horizons that are more than one month ahead using the mean square error, and the aggregated ETS has better forecasting ability for horizons equal to 1 and 2. The aggregated ETS forecast does not contain information that is useful for forecasting industrial production in Brazil beyond the information already found in the disaggregated ETS forecast between two and twelve months ahead.

中文翻译：

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使用聚合和分解系列或两者的组合预测工业生产：来自一个新兴市场经济的证据

在本文中，我们讨论了与单独使用聚合序列相比，使用分解序列还是组合聚合序列和分解序列可以改善聚合序列的预测。我们使用计量经济学技术，例如加权滞后自适应最小绝对收缩和选择算子、指数三次平滑 (ETS) 以及 Autometrics 算法来预测巴西未来一到十二个月的工业生产。这是这项工作的新颖之处，因为使用了平均多视野高级预测能力（aSPA）和统一多视野高级预测能力（uSPA）测试，用于通过组合不同的视野来选择最佳预测模型。我们的样本涵盖 2002 年 1 月至 2020 年 2 月期间。