• KDI Journal of Economic Policy
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• v.34 no.4
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• pp.117-156
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• 2012

This paper decomposes and estimates trend/cyclical components of some key macro variables-GDP, inflation, and interest rate, using a simple DSGE model along with flexible trend specification. The extracted cyclical components of output and interest rate are similar to HP-filtered counterparts, despite some differences in persistence and volatility, while inflation resembles that from BK filtering. This implies that the usual practice of applying a single filtering method to the data of interest may be problematic. When the baseline model is extended to incorporate consumption habit and price indexation, habit turns out to be important in explaining the persistence of business cycles. Comparison of several alternative models shows that the usual practice of estimation of DSGE model using filtered data leads to biased results. Finally, various sensitivity analyses illustrate that (1) allowing for correlation between structural cyclical shocks and trend shocks and (2) including irregular components (in inflation rate) may deliver interesting/important implication for gap estimates.

• Journal of Korea Port Economic Association
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• v.38 no.3
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• pp.87-104
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• 2022

Considering the complex structure and high volatility in the shipping market, it is important to investigate the connectedness amongst influencing factors. This study explores the dynamic relationship between supply-demand factors and shipping freight indices. We choose Capesize and Panamax in the bulk carrier market and use quarterly data of GDP, world fleet, BCI, and BPI from 1999 to 2021. Applying the wavelet analysis and wavelet Granger causality test, the simultaneous examination of co-movement and causality between two factors and the shipping market in both the time and frequency domains is achieved. We find that co-movement and causality vary across time and frequencies, thereby existing dynamic relationships between variables. Second, compared to multiple coherencies using demand and supply factors together, partial coherencies indicate noticeable causalities. It implies that analyzing demand and supply factors separately is essential. Finally, shipping freight indices show a high correlation with the demand factor in a good market and with the supply factor in a bad market. Generally, GDP positively leads shipping freights in the recovery phase while the world fleet negatively leads shipping freights in the downturn. The research is meaningful in that the rarely-applied wavelet analysis is adopted in the shipping market and that it gives a reasonable ground to explain the role of supply and/or demand factors in different phases of the market cycle.

• KDI Journal of Economic Policy
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• v.31 no.2
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• pp.47-85
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• 2009

With a relatively simple quantitative method, this study comprehensively analyzes the characteristics related to business cycles represented by macroeconomic variables of Korea since 1970. This empirical analysis deals with roughly following three topics: How to identify cyclical component with respect to trend; with what characteristics and how the economic variables of each sector move with in the phases of business cycle, and; whether there are signs of a structural change in the phases of business cycle. Section 2 discusses how to identify trends and cycle components, the basis assumption for the analysis of business cycle. Like the Korean economy, where a relatively high growth rate has been maintained, it is appropriate to determine its economic recession based on the fall in the growth trend, not in the absolute level of real output. And, it is necessary to apply the concept of growth cycle against a traditional concept of business cycle. Accordingly the setting of growth trend is of preliminary importance in identifying cyclical fluctuations. The analysis of Korea's GDP data since 1970, the decomposition of trends and cycles through the Band-pass filter is found to appropriately identify the actual phases of busyness cycle. Section 3 analyzes what particular relationship various economic variables have with output fluctuations during the phases of economic cycle, using the corss-correlation coefficients and prediction contribution. Section 4 monitors the stability of the phases of Korea's business cycle and quantitatively verifies whether there is a structural break, and then reviews the characteristics of variations in each sector. And, stylized facts observed through these studies are summarized in the conclusion. The macroeconomic stability of Korea, in particular, is found to continue to improve since 1970, except for the financial crisis period. Not only that, it is found that its volatility of economic growth rate as well as inflation have been reduced gradually. Meanwhile, until recently since 2000, the volatility in domestic demand has remained stable, while that in exports and imports has been increased slightly. But, in an over all perspective, Korea's business cycle variation is on the decline due to shorter response period to shocks and the formation of complementary relationship among economic sectors.

• Korea Trade Review
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• v.43 no.4
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• pp.155-175
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• 2018

This study estimates the technical efficiency and total factor productivity(TFP) of and analyzes the relationship between TFP and exports for Korean manufacturing companies from 2000 to 2016. Specially, TFP is decomposed into Technical Change(TC), Technical Efficiency Change (TEC), and Sale Effect(SE), and compared between large and small enterprises. First, in the case of technical efficiency, the Korean economy has been very vulnerable to external shocks, such as the sharp decline following the 2008 financial crisis. The efficiency of the electronics, automobile, and machinery sectors is low and needs to be improved. In addition, the technological efficiency of large enterprises is higher than that of SMEs in most manufacturing sub-sectors except for non-ferrous metals. In the case of TFP, most changes are due to TC, and the effective combination of labor, capital and the effect of scale have little effect, suggesting that improvement of internal structure is urgent. In addition, volatility due to the impact of the financial crisis in 2008 was much larger in SMEs than in large companies, so external economic impacts are more greater for SMEs than large enterprises. The relationship between TFP decomposition factors and exports shows that TC has a positive effect only on exports of SMEs. Therefore, in order to increase exports, in the case of SMEs, R&D support to promote technological development is needed. In the case of large companies, it is necessary to establish differentiated strategies for each export market, competitor company, and item to link efficiency and scale effect of exports.