Purpose

The purpose of this paper is to examine the long-term cointegrating relationship between ocean, rail, truck and air cargo freight rates, as well as the short-term dynamics between these four series. The authors also test the predictive ability of these freight rates on major economic indicators.

Design/methodology/approach

The authors employ a vector error-correction model using 16 years of monthly time series data on freight rate data in the ocean, truck, rail and air cargo sectors to examine the interrelationship between these series as well as their interrelationship with major economic indicators.

Findings

The authors find that truck freight rates and as well as dry bulk freight rates have the strongest predictive power over other transportation freight rates as well as for the four major economic indicators used in this study. The authors find that dry bulk freight rates lead other freight rates in the short-run but lag other freight rates in the long run.

Originality/value

While ocean freight rate time series have been examined in a large number of studies, little research has been done on the interrelationship between ocean freight rates and the freight rates of other modes of transportation. Through the use of data on five different freight rate series, the authors are able to assess which rates lead and which rates lag each other and thus assist future researchers and practitioners forecast freight rates. The authors are also one of the few studies to assess the predictive power of non-ocean freight rates on major economic indicators.

中文翻译：

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海运、铁路、卡车和空运价格之间的相互关系

目的

本文的目的是检验海运、铁路、卡车和空运运费之间的长期协整关系，以及这四个系列之间的短期动态。作者还测试了这些运费对主要经济指标的预测能力。

设计/方法/方法

作者采用矢量误差校正模型，使用 16 年的海运、卡车、铁路和航空货运部门运费数据的月度时间序列数据来检验这些序列之间的相互关系以及它们与主要经济指标的相互关系。

发现

作者发现，卡车运费和干散货运费对其他运输运费以及本研究中使用的四个主要经济指标的预测能力最强。作者发现，干散货运费在短期内领先于其他运费，但从长期来看落后于其他运费。

原创性/价值

虽然海运费时间序列已在大量研究中得到检验，但关于海运费与其他运输方式运费之间的相互关系的研究却很少。通过使用五个不同运价系列的数据，作者能够评估哪些运价领先，哪些运价相互滞后，从而帮助未来的研究人员和从业人员预测运价。作者也是为数不多的评估非海运运费对主要经济指标预测能力的研究之一。