用产出、商品价格和商品货币数据衡量持续存在的全球经济因素

IF 3.4 3区 经济学 Q1 ECONOMICS
Arabinda Basistha, Richard Startz
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引用次数: 0

摘要

在本研究中,我们在动态因素模型中使用了七国集团月度工业生产数据、商品价格指数数据和商品货币汇率数据,以研究对商品价格预测有用的全球经济因素。我们通过指定一个持久性因子和一个非持久性因子来区分动态因子,既有使用所有数据的单一全球因子,也有针对每类数据的因子。三个持久性因子在样本内的预测效果均优于非持久性因子和单一全局因子。基于预测组合的样本外结果也支持持久性因子对整体商品价格和大多数子类别商品价格指数的预测信息。预测准确性的提高是多方面的,在最近的样本中,总体商品价格在 1 到 6 个月的范围内提高了 5%到 7%,化肥价格在 12 个月的范围内提高了约 20%。我们进一步表明,持久性因子中的信息,尤其是基于商品货币汇率的持久性因子中的信息,可以与其他全球指标相结合,进一步提高全球指标的预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring persistent global economic factors with output, commodity price, and commodity currency data

In this study, we use monthly G7 industrial production data, commodity price index data, and commodity currency exchange rate data in a dynamic factor model to examine the global economic factors useful for commodity price prediction. We differentiate between the dynamic factors by specifying a persistent factor and a non-persistent factor, both as a single global factor using all data and as factors for each category of data. The in-sample predictive performances of the three persistent factors together are better than the non-persistent factors and the single global factors. Out-of-sample outcomes based on forecast combinations also support the presence of predictive information in the persistent factors for overall commodity prices and for most sub-categories of commodity price indexes relative to their means. The gains in forecast accuracy are heterogeneous, ranging from 5% to 7% in the 1- to 6-month horizon for overall commodity prices to a high of around 20% for fertilizers in the 12-month horizon in the recent sample. We further show that the information in the persistent factors, especially in the commodity currency exchange rate-based persistent factor, can be integrated with other global measures to further improve the predictive performances of the global measures.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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