Asymmetric impact of oil price on trade balance in BRICS countries: Multiplier dynamic analysis

Abstract

This paper examines the impact of oil prices on the trade balance by incorporating wholesale prices and an industrial production proxy for economic activity as supporting variables for the BRICS countries. Quarter frequency data from 1992 to 2015 is used. Keeping in view the structural breaks and resulting asymmetries, long‐run and short‐run dynamic estimations are conducted using the nonlinear ARDL approach proposed by Shin, Yu, and Greenwood‐Nimmo. We find a positive and significant asymmetric relationship between oil prices and the trade balance in Brazil, India, China and South Africa. The asymmetries of wholesale prices also have a positive and significant effect on the trade balance in the cases of Russia, India and South Africa. The asymmetries of industrial production have a significant but negative impact on the trade balance for India and South Africa. These nonlinear and diverse relationships of oil prices, wholesale prices and economic activity with the trade balance are also confirmed by long‐run parameters for BRICS countries. This study opens some new insights for policy making.

Exploring the Asymmetrical Influence of Economic Growth, Oil Price, Consumer Price Index and Industrial Production on the Trade Deficit in China

“…Similarly, the calculated long-run coefficients of the consumer price index (Ln CPI + t and Ln CPI − t ) reveal that a 1% increase and decline in the consumer price index, or inflation, increase the trade deficit by 1.253% and 3.119%, respectively, which reveals that the consumer price index does not have an asymmetrical impact on the trade deficit. These results support Ahad [16] and Islam [78]. The logical reason is that a rise in the consumer price index, or inflation, decreases people's purchasing power in China.…”

“…The reported outcomes in Table 6 clearly show that, for the trade deficit, industrial production, oil price, consumer price index, and GDP growth rate, the null hypothesis of linearity is rejected at a 1% level of significance, denoting that the overall described series is non-linear and not identically distributed, which reflects the existence of severe asymmetries. Further, Ahad and Anwer [16] and Liu, et al [70] highlighted that when data series contain a mixed order of integration, uni-directional causality, and dynamic asymmetries, then the basic ARDL bounds testing approach is invalid for exploring long-and short-run association among variables. Therefore, to resolve this issue, the NARDL model [71] is employed to measure the long-and short-run relationship among the variables.…”

“…Due to upsurges in financial activities and population, oil consumption and imports are increasing daily worldwide. In the last decade of the 20th century, i.e., the period from 1990 to 2000, global crude oil imports were around USD 579.4 billion, which were further increased to USD 2394.1 billion in 2008 and USD 2616.6 billion in 2011 [16]. The importance of the linkage of oil prices with the balance of trade strongly relies on whether the state is an oil exporter or an importer.…”

The Dynamic Effect of Financial Sector Development in Stimulating the Gross National Savings of Djibouti

“…/mdke-2023 linear and not randomly dispersed, reflecting the presence of significant asymmetries. Moreover, Liu et al (2020), as well as Ahad and Anwer (2021) noted that the basic ARDL limits assessment technique is inefficient for assessing long-and short-run connections across variables when sample data comprises a blended sequence of integration, one-way causality, and dynamic asymmetries. As a result, in this scenario, the NARDL model is used to assess the long-and short-run correlations between the variables and to address this issue.…”