November 25, 2024
Reza Roshan

Reza Roshan

Academic Rank: Associate professor
Address:
Degree: Ph.D in -
Phone: -
Faculty: School of Business and Economics

Research

Title Investigating the Impact of Macroeconomic Variables on the Container Trading in Iranian ports of the Persian Gulf
Type Article
Keywords
Container Trading, Iranian Ports in the Persian Gulf, Macroeconomic Variables, ARDL Approach
Journal International Journal of Business and Development Studies
DOI 10.22111/IJBDS.2022.7475
Researchers Reza Roshan (First researcher)

Abstract

Nowadays, maritime trade plays an important role in the economy of some countries, including Iran. Especially, some of the ports in Iran that are located on the Persian Gulf border carry out container shipping operations. Therefore, in the present study, we investigate the impact of the most important macroeconomic factors on the container trading in Iranian ports in the Persian Gulf. For this purpose, we employed the autoregressive distributive lag (ARDL) approach for the estimating effect of oil price, gross national production, industrialization and exchange rate on the container trading volume. The used data are quarterly. Container business data is related to the ports of Shahid Rajaei, Bushehr, Imam Khomeini, Khorramshahr, Shahid Bahonar, which are obtained from the Ports and Maritime Organization. The results of this study show a negative impact of increasing oil price on Iran's container trading. Moreover, our empirical findings indicate that the effect of industrial indicator (INDS) on the container trading volume is positively. Also, one percent increases in gross national production (GNP) leads to %0.37 increase in container trading, and one percent increase in exchange rate (EXH) leads to %.17 decrease in container trading, respectively. Finally, coefficient of ECM is -0.54. It means that speed of adjustment in the function of container trading volume is relatively high, and in each period, 54 percent deviation from long run direction of container trading volume to be corrected by variables of model.