通过港口反映经济活动:以澳大利亚为例

IF 3.9 Q2 TRANSPORTATION Maritime Transport Research Pub Date : 2021-01-01 DOI:10.1016/j.martra.2021.100021
Jason Angelopoulos , Thomas Vitsounis , Persa Paflioti , Constantinos Chlomoudis , Ioannis Tsmourgelis
{"title":"通过港口反映经济活动:以澳大利亚为例","authors":"Jason Angelopoulos ,&nbsp;Thomas Vitsounis ,&nbsp;Persa Paflioti ,&nbsp;Constantinos Chlomoudis ,&nbsp;Ioannis Tsmourgelis","doi":"10.1016/j.martra.2021.100021","DOIUrl":null,"url":null,"abstract":"<div><p>With approximately 85% of global trade moved by sea, the relationship between ports and the economy has become symbiotic. Identifying and tracking this relationship is sought by both port economics and port forecasting literature. Tackling both challenges -i.e., the ports-economy relationship and forecasting- at once, can only be pursued by data-driven factor models, through their ability to reduce the dimensionality of large cross sections of time series. We find fertile ground in applying, for the first time, a factor modeling approach to the Australian port sector by utilizing a disaggregate dataset of 2765 series representing national and regional port activity for 20 years. Through our model, we establish a quantifiable connection between ports and the economy and demonstrate their capacity in reflecting economic activity. We assess a rich lead-lag structure in our dataset and trace its cyclical properties. Using the same method, we compare the Australian and U.S port sectors, revealing insights on their structural differences. Finally, utilizing our model as a forecasting device, we report favorable short and mid-term forecasting performance against benchmarks.</p></div>","PeriodicalId":100885,"journal":{"name":"Maritime Transport Research","volume":"2 ","pages":"Article 100021"},"PeriodicalIF":3.9000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.martra.2021.100021","citationCount":"3","resultStr":"{\"title\":\"Reflecting economic activity through ports: The case of Australia\",\"authors\":\"Jason Angelopoulos ,&nbsp;Thomas Vitsounis ,&nbsp;Persa Paflioti ,&nbsp;Constantinos Chlomoudis ,&nbsp;Ioannis Tsmourgelis\",\"doi\":\"10.1016/j.martra.2021.100021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With approximately 85% of global trade moved by sea, the relationship between ports and the economy has become symbiotic. Identifying and tracking this relationship is sought by both port economics and port forecasting literature. Tackling both challenges -i.e., the ports-economy relationship and forecasting- at once, can only be pursued by data-driven factor models, through their ability to reduce the dimensionality of large cross sections of time series. We find fertile ground in applying, for the first time, a factor modeling approach to the Australian port sector by utilizing a disaggregate dataset of 2765 series representing national and regional port activity for 20 years. Through our model, we establish a quantifiable connection between ports and the economy and demonstrate their capacity in reflecting economic activity. We assess a rich lead-lag structure in our dataset and trace its cyclical properties. Using the same method, we compare the Australian and U.S port sectors, revealing insights on their structural differences. Finally, utilizing our model as a forecasting device, we report favorable short and mid-term forecasting performance against benchmarks.</p></div>\",\"PeriodicalId\":100885,\"journal\":{\"name\":\"Maritime Transport Research\",\"volume\":\"2 \",\"pages\":\"Article 100021\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.martra.2021.100021\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Maritime Transport Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666822X21000137\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Maritime Transport Research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666822X21000137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 3

摘要

随着全球约85%的贸易通过海运进行,港口与经济之间的关系已变得共生。港口经济学和港口预测文献都在寻求识别和跟踪这种关系。同时应对这两个挑战,即港口经济关系和预测,只能通过数据驱动的因素模型来实现,因为它们能够降低时间序列大截面的维度。我们发现,通过利用代表20年来国家和地区港口活动的2765个系列的分解数据集,首次将因素建模方法应用于澳大利亚港口部门是一片沃土。通过我们的模型,我们在港口和经济之间建立了可量化的联系,并展示了它们反映经济活动的能力。我们在数据集中评估了一个丰富的超前-滞后结构,并追踪了其周期性特性。使用相同的方法,我们比较了澳大利亚和美国的港口部门,揭示了它们的结构差异。最后,利用我们的模型作为预测设备,我们报告了相对于基准的有利的短期和中期预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Reflecting economic activity through ports: The case of Australia

With approximately 85% of global trade moved by sea, the relationship between ports and the economy has become symbiotic. Identifying and tracking this relationship is sought by both port economics and port forecasting literature. Tackling both challenges -i.e., the ports-economy relationship and forecasting- at once, can only be pursued by data-driven factor models, through their ability to reduce the dimensionality of large cross sections of time series. We find fertile ground in applying, for the first time, a factor modeling approach to the Australian port sector by utilizing a disaggregate dataset of 2765 series representing national and regional port activity for 20 years. Through our model, we establish a quantifiable connection between ports and the economy and demonstrate their capacity in reflecting economic activity. We assess a rich lead-lag structure in our dataset and trace its cyclical properties. Using the same method, we compare the Australian and U.S port sectors, revealing insights on their structural differences. Finally, utilizing our model as a forecasting device, we report favorable short and mid-term forecasting performance against benchmarks.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.90
自引率
0.00%
发文量
0
期刊最新文献
Ports as business eco-systems in transition Big data and artificial intelligence in maritime transport research Multi-objective vessel routing problems with safety considerations: A review Sustainable maritime shipping Operations Research in Maritime Logistics
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1