Business cycle synchronization and its determinants in the OECD countries: panel data evidence

ABSTRACT

The primary determinants of business cycle synchronization among OECD countries and their interactions are examined using panel Granger causality tests, single-equation panel regressions, simultaneous equations model error component three-stage least squares (EC3SLS), and cross-sectional 3SLS econometric techniques. The sample spans the years 1990 through 2021. The results demonstrate the direct and indirect effects of the key macroeconomic variables on the synchronization of the business cycles of the sample economies, as well as the complementary and competing nature of these variables. The magnitude and significance of these macroeconomic factors in relation to the business cycle synchronization before and after the global financial crisis of 2009 are also examined in this study. Furthermore, the analysis supports the ex-post argument for entering the euro currency union. The study article contributes by providing direction for future empirical research on the sources and propagation mechanisms of international business cycle transmission.

KEYWORDS:

• Business cycle movements
• bilateral trade
• industrial dissimilarity
• FDI
• OECD countries

JEL CLASSIFICATION:

• E32
• F02
• F10
• C23
• C33

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ The 19 OECD countries used in this study were chosen based on data availability. Austria, Canada, Denmark, Finland, France, Germany, Greece, Italy, Japan, Luxembourg, the Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland, the United Kingdom, and the United States are among these countries. Data from these sample economies is being used to examine the post-crisis period. To evaluate the pre-2009 global financial crisis period and the eurozone economies, a subset of 15 OECD countries is used, excluding Canada, Finland, Greece, and Luxembourg due to missing data for some variables/time periods. The former produces$N\times \left(N-1\right)/2=171$ country-pairs with 2903 sample observations, while the latter produces$N\times \left(N-1\right)/2=105$ country-pairs resulting in 735 observations. Table 4 shows the outcomes of 105 country-pairs from 1990 to 2021.

² One advantage of averaged data groups is that it reduces the chances of autocorrelation in the data series.

³ Rose (2017) provides a meta-analysis of studies devoted to finding the EMU/Euro effect on trade.

⁴ Kunroo (2015) supplies a generous survey of the various criteria of an optimum currency area (OCA).