• Journal of Korean Society of Forest Science
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• v.99 no.4
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• pp.573-579
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• 2010

This study was carried out to investigate the relationship between visitor uses of national parks and economic variables, such as the index of industrial product and the consumer price index. The results from the Granger Causality test showed that the index of industrial product and the consumer price index influenced visitor use at national parks. Also the Impulse Response Analysis showed that the index of industrial product and the consumer price index greatly influenced national park visitor use in the short term as well as the long term. The study showed that national park visitor use was mainly influenced by variance decompositions. These results suggested that economic variables could be used to not only forecast the demand for recreation but also establish recreational policies.

• International Area Studies Review
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• v.13 no.3
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• pp.309-330
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• 2009

This paper analyzes comparatively the effects of changes in real effective exchange rates on the business fluctuations of the cases of Korea and Japan employing structural vector auto-regression(S-VAR) model which uses quarterly data for the five variables of real effective exchange rates, GDP gap, real interest rates, oil prices, inflation rates for the period of 1980-2006. The paper employes impulse-response analysis and variance decompositions. The paper finds that real exchange rate depreciations are contractionay for the case of Korea while they are expansionary for the case of Japan. These results are consistent with the prevailing empirical results that real exchange rate depreciations are contractionary for developing countries while expansionary for advanced countries.

• Journal of Korea Port Economic Association
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• v.20 no.1
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• pp.43-55
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• 2004

This paper investigates how Japanese economic shocks affect the Korean economy and analyzes the channels through which they are transmitted. Also, the relative importance of domestic and foreign shocks on the dynamics of certain key macro variables is investigated. The techniques of vector autoregression (VAR) are employed to investigate the international transmission of economic disturbances. The VAR methodology is a particularly useful means for characterizing the dynamic relationships among economic variables without imposing certain types of theoretical restrictions. The dynamic effects of Japanese economic shocks on the Korean economy are evaluated by estimating variance decompositions (VDCs) and impulse response functions (IRFs). This study supports the notion of economic dependence of a small open economy such as Korea to a large economy such as Japan.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.35-48
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• 2015

We analyze the systemic risk based on the information flows using the variance decomposition, DebtRank methods, and the Industry Sector Indices during 2001. 01 to 2015. 08. Using the KOSPI stock market as our setting, we find that (i) the systemic risk calculated by information flows of variance decompositions method shows strong positive relations with the market volatility, (ii) the magnitude of systemic risk measured from the information flows network by DebtRank method increases after the subprime financial crisis.

• Journal of Korea Port Economic Association
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• v.19 no.2
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• pp.55-67
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• 2003

The purpose of this study is to estimate and forecast the marine trading volumes based on the structural model. We employ GPH cointegration test since the structural model must be stationary to get the accurate predicted values. The empirical results show that our model is stationary. This paper also applies variance decompositions and impulse-response functions to the structural model composed of exchange rate, domestic industrial activity, and world business. The results indicate that while both loading and unloading volumes respond positively to the shocks in income and then decay very slowly, their responses are different to the shocks in exchange tate.

• International Area Studies Review
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• v.15 no.3
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• pp.175-188
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• 2011

This study aims to examine how the U.S. economic shocks affect the Japanese economy. It is widely believed that the U.S. economy has a significant effect on the Japanese economy. Actually, the U.S. accounts for a considerable amount of Japan's exports and imports. To the economic policymakers, it is very important to know how economic disturbances generated by the U.S. are transmitted to the Japanese economy. A vector autoregression(VAR) model is employed to investigate the international transmission channel of economic disturbances. The interactions of the U.S.-Japansese economy are investigated by using variance decompositions(VDCs). The results of this study provided the evidence that the U.S. economic shocks were important for the Japanese economy during the sample period. This study supports the notion of economic dependence of smaller open economy such as Japan as compared with larger economy such as the U.S.

• Korea Trade Review
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• v.46 no.5
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• pp.115-130
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• 2021

The main purpose of this study is to analyze the causal relationship between import-export goods and transportation modes. To this end, five major commodity groups were selected from 2010 to 2018 such as Machinery and transport equipment (SITC 7), manufactured goods classified chiefly by material (SITC 6), chemicals and related products, n.e.s. (SITC 5), mineral, fuels, lubricants, and related materials (SITC 3), and miscellaneous manufactured articles (SITC 8). And using the panel VECM, the difference between transportation modes such as ports and airports was compared and analyzed through panel granger causality, Impulse response function, Forecasting error variance decomposition. As a result, it is confirmed that the causal relationship between major product groups and transportation modes showed different causal relationships depending on the characteristics of port and air transportation.

• Journal of Korea Port Economic Association
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• v.25 no.3
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• pp.269-282
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• 2009

This study investigates the difference of behavioral patterns between the import container volume of all ports and that of Gwangyang port in Korea. All series span the period January 1999 to December 2008. I first test whether the series are stationary or not. I can reject the null hypothesis of a unit root in each of the level variables and of a unit root for the residuals from the cointegration at the 5 percent significance level. I hitherto make use of variance decompositions and impulse response functions, both of which have now been widely used to examine how much movement in one variable can be explained by innovations in different variables and how rapidly these fluctuations in one variable can be transmitted to another. The variance decompositions for the import container volume show that the proportions of the forecast error variance of import container volumes explained by themselves are 30 and 26 per cent after 12 months, respectively. As a result, innovations in exchange rate and business activity explain 70 and 74 per cent of the variance in the import container volume. All in all, innovation accounting indicates that import container volumes are not exogenous with respect to exchange rate and business activity. The impulse responses indicate that container volumes decrease sharply to the shocks in exchange rate and decay very slowly to its pre-shock level, while container volumes respond positively to the shocks in the business activity and disappear very slowly, showing that the shocks last very long. Furthermore Gwangyang port is more sensitive to the change of the exchange rate and the industrial production than all ports.

• Journal of Korea Port Economic Association
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• v.23 no.1
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• pp.1-18
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• 2007

The purpose of this study is to examine the effect of exchange rate volatility on Trading Volume of Container of Korea, and to induce policy implication in the contex of GARCH and regression model. In order to test whether time series data is stationary and the model is fitness or not, we put in operation unit root test, cointegration test. And we apply impulse response functions and variance decomposition to the structural model to estimate dynamic short run behavior of variables. The major empirical results of the study show that the increase in exchange rate volatility exerts a significant negative effect on Trading Volume of Container in long run. The results Granger causality based on an error correction model indicate that uni-directional causality between trading volume of container and exchange rate volatility is detected. This study applies impulse response function and variance decompositions to get additional information regarding the Trading Volume of Container to shocks in exchange rate volatility. The results indicate that the impact of exchange rate volatility on Trading Volume of Container is negative and converges on a stable negative equilibrium in short-run. Th exchange rate volatility have a large impact on variance of Trading Volume of Container, the effect of exchange rate volatility is small in very short run but become larger with time. We can infer policy suggestion as follows; we must make a stable policy of exchange rate to get more Trading Volume of Container

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.281-296
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• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.