The interrelationship between ocean, rail, truck and air freight rates. Issue 3 (29th June 2021)

• Record Type: Journal Article
• Authors: Shackman, Joshua; Dai, Quinton; Schumacher-Dowell, Baxter; Tobin, Joshua
• Is Part Of: Maritime business review. Volume 6:Issue 3(2021)
• Page Start: 256
• Page End: 267
• Publication Date: 2021-06-29
• Subjects: Cointegration -- Time series -- Air freight rates -- Ground freight rates; Shipping -- Periodicals; 387.5
• DOI: 10.1108/MABR-08-2020-0047
• Languages: English
• ISSNs: 2397-3757

Abstract

Abstract : 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.