{"title":"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","authors":"Arabinda Basistha, Richard Startz","doi":"10.1002/for.3139","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2860-2885"},"PeriodicalIF":3.4000,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Measuring persistent global economic factors with output, commodity price, and commodity currency data\",\"authors\":\"Arabinda Basistha, Richard Startz\",\"doi\":\"10.1002/for.3139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":47835,\"journal\":{\"name\":\"Journal of Forecasting\",\"volume\":\"43 7\",\"pages\":\"2860-2885\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/for.3139\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/for.3139","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
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.
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.