Oil price changes and aggregate economic fluctuations: new evidence from the Republic of Korea

ABSTRACT

The impact of oil price on the aggregate economy is an interesting topic which has been examined mainly in the time dimension, but relatively little exploration has been done to reveal how oil price influences inflation, industrial production, and unemployment across frequencies and over time. Employing a continuous wavelet approach, this study contributes to the literature by investigating the effects of oil price on aggregate economic activities in the time-frequency space with a monthly dataset from South Korea. The empirical results demonstrate that: First, the effect of oil price on inflation becomes stronger as moving from high frequencies between 2000 and 2005 to low frequencies between 2008 and 2018. Second, an increase in oil price is associated with a decrease in industrial production, and oil price movements could largely mirror industrial production fluctuations at the 64-month scale between 2011 and 2013 and at the 64-96-month scale between 2014 and 2021. Third, the impact of oil price on unemployment becomes weaker when moving from the 12-24-month scale between 2009 and 2012 to the 24-48-month scale between 2008 and 2016. Our results indicate that policymakers should consider these heterogeneous effects of oil price on the aggregate economy while developing stabilization policies.

KEYWORDS:

• Oil price
• inflation
• industrial production
• unemployment
• continuous wavelet multiple and partial coherency

JEL CLASSIFICATION:

• C49
• E27
• E32
• Q41
• O53

Acknowledgement

We would like to sincerely thank the two anonymous referees and the editor for their very constructive comments and extremely helpful suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

¹ The main result of Meng (2020) is that an increase in oil price leads to productivity growth slowdown and unemployment increase at the scale of 16–24 months after 1990 for Japan and 8–24 months after 2005 for US. However, we find that oil price generates two different categories of effects on unemployment. First, an oil price increase results in an unemployment decrease, which is opposite to Meng (2020). Moreover, this negative effect decreases from high to low frequencies. Second, an oil price increase also leads to unemployment increase at the 12-24-month scale between 2013 and 2016 in Korea, which is similar to Meng (2020), but the effect in Korea fluctuates significantly in the time-frequency space..

² One exception is Meng (2020), he examined the effect of oil price on unemployment when studying how unemployment depends on its determinants in Japan and US, but he didn’t evaluate the effects of oil price on other macroeconomic activities.

³ In evaluating how unemployment depends on its determinants, Meng (2020) studied the effect of oil price on unemployment in the time-frequency space for Japan and US, but his theoretical foundations, controls, and findings are different from ours..

⁴ The DCC-GJR-GARCH model stands for dynamic conditional correlation Glosten-Jagannathan-Runkle generalized conditional heteroskedasticity (DCC-GJR-GARCH) model.

⁵ We only provide a very brief description of the wavelet multiple and partial analysis in this section, please refer to Aguiar‐conraria and Soares (2014) for details.

⁶ We use the consumer price index of Korea to calculate the inflation rate, as INFL is one of the most important macroeconomic variables to be studied.

⁷ Since all variables under investigation are stationary, there is no need to conduct cointegration test among these time series..

⁸ The cone of influence also contains the region that is shaded from the line edge to the time axis and the scale axis, which illustrates regions that could be influenced by edge effect.

⁹ We will omit the word ‘wavelet’ in all relevant terminologies for simplicity. For example, we would use partial gains instead of wavelet partial gains without loss of generality.