• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.141-154
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• 2002

Foreign investors'shares and their influences on the Korean stock market have never been larger and greater before since the market was completely open to foreign investors in 1992 Quantitatively and qualitatively as well, as a result, changes in the patterns of foreign investments have caused enormous effects on the interactions of major macroeconomic indices of the Korean economy. This paper is intended to investigate the causal relations of the four variables, foreigners'buy-sell ratios, stock returns, ￦/＄ exchange rates and $\yen$/＄ exchange rates, over the two time periods of the pre-IMF (1996.1.1-1997.8.15) and the post-IMF (1997.8.16-2000.6.15) based on the daily data of the variables. Granger Causality Test, Forecast Error Variance Decomposition(FEVD) using VAR model and Impulse Response Function were implemented for the empirical analysis.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.76-84
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• 2024

This study constructed a VAR model with cotton futures and spot price data from April 30, 2009 to November 16, 2022, for empirical analysis utilizing the Granger causality test to analyze the dynamic relationship between cotton futures and spot market prices in China. The impulse response function and variance decomposition analysis showed that the cotton spot prices at flowering have a causal relationship with each other; in terms of mutual influence and impact, futures prices are higher than spot prices. Finally, it proposed countermeasures and suggestions from the perspective of establishing a standardized cotton spot market, improving the laws and regulations of the cotton futures market and trading system, and optimizing the structure of investment subjects.

• International Area Studies Review
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• v.13 no.2
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• pp.255-276
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• 2009

This article has examined the international transmission of returns among S&P500, Nikkei225 and SENSEX stock index cash markets using the daily closing prices covered from January 4, 2002 to February 6, 2009. For this purpose we employed dynamic time series models such as the Granger causality analysis and variance decomposition analysis based on VAR model. The main empirical results are as follows; First, according to Granger causality tests we find that S&P500 stock index has a significant prediction power on the changes of SENSEX and Nikkei225 stock index market and vice versa. However, US stock market's influence is dominant to the other stock markets at a significant level statistically. Second, according to variance decomposition, SENSEX stock index is more sensitive to the movement of S&P500 than that of Nikkei225 stock index. These kinds of empirical results shows that the three stock markets are integrated over times and these results will be informative for the international investors to build the world-wide investment portfolio and risk management strategies, etc.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.255-289
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• 2011

This study made linked Energy IO Table 00-05-08 of 76 sectors in intermediate sectors and analyzed structural decomposition analysis in energy consumption change in industrial sector with both by aggregate data and micro data. Structural decomposition analysis focused value added level change, value added share change of each industry, output structural change of each industry and energy intensity change of each industry as factors. Supply side model based on Ghosh inverse matrix was applied as empirical model because Korea has export driven industrial structure. Empirical results with aggregate data showed that value added change increased energy consumption and output structural change of each industry decreased energy consumption in both 2000~2005 and 2005~2008. However value added share change and energy intensity change caused opposite direction in energy consumption change with time. Policy based on aggregate data can not evaluate effort of each industry in energy efficiency and make effective results because aggregate data delete character of each industry.

• Journal of Korean Society of Rural Planning
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• v.27 no.4
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• pp.1-12
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• 2021

The purpose of this study is to analyze the effect of crop diversification on farm households' agricultural income. Abundant literature have explored the determinants and efficient strategies for crop diversification. Yet, there is a paucity of research studies that empirically test the effectiveness of crop diversification as a profitable farm management strategy. Utilizing the 2015 Agricultural Census, this study adopts a quasi-experimental research design to compare the outcomes between farm households that opted for crop diversification and farm households that did not engage in such a strategy. In doing so, this study applies the Heckman Selection Model and the decomposition technique to address the problem of selection bias and to identify the causal effect. Our empirical results show that farms that implement diversification are more likely to earn higher agricultural income than non-diversified farms, although the difference would not be much substantial. This study concludes with several policy proposals to stabilize agricultural income in conjunction with crop diversification.

• International Journal of Naval Architecture and Ocean Engineering
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• v.9 no.6
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• pp.624-640
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• 2017

In this paper, the slamming coefficients on local members of a jacket structure under plunging breaker are studied based on numerical simulations. A 3D numerical model is used to investigate breaking wave forces on the local members of the jacket structure. A wide range of breaking wave conditions is considered in order to get generalized slamming coefficients on the jacket structure. In order to make quantitative comparison between CFD model and experimental data, Empirical Mode Decomposition (EMD) is employed for obtaining net breaking wave forces from the measured response, and the filtered results are compared with the computed results in order to confirm the accuracy of the numerical model. Based on the validated results, the slamming coefficients on the local members (front and back vertical members, front and back inclined members, and side inclined members) are estimated. The distribution of the slamming coefficients on local members is also discussed.

• Journal of the Korean Geographical Society
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• v.49 no.5
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• pp.675-689
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• 2014

This study applied Ensemble Empirical Mode Decomposition(EEMD), a new methodology to define the timing of spring onset over the Republic of Korea and to examine its spatio-temporal change. Also this study identified the relationship between spring onet timing and some atmospheric variations, and figured out synoptic factors which affect the timing of spring onset. The averaged spring onset timing for the period of 1974-2011 was 11th, March in Republic of Korea. In general, the spring onset timing was later with higher latitude and altitude regions, and it was later in inland regions than in costal ones. The correlation analysis has been carried out to find out the factors which affect spring onset timing, and global annual mean temperature, Arctic Oscillation(AO), Siberian High had a significant correlation with spring onset timing. The multiple regression analysis was conducted with three indices which were related to spring onset timing, and the model explained 64.7%. As a result of multiple regression analysis, the effect of annual mean temperature was the greatest and that of AO was the second. To find out synoptic factors affecting spring onset timing, the synoptic analysis has been carried out. As a result the intensity of meridional circulation represented as the major factor affect spring onset timing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.165-174
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• 2019

As a nonstationarity is observed in hydrological data, various studies on nonstationary frequency analysis for hydraulic structure design have been actively conducted. Although the inherent diversity in the atmosphere-ocean system is known to be related to the nonstationary phenomena, a nonstationary frequency analysis is generally performed based on the linear trend. In this study, a nonstationary frequency analysis was performed using climate indices as covariates to consider the climate variability and the long-term trend of the extreme rainfall. For 11 weather stations where the trend was detected, the long-term trend within the annual maximum rainfall data was extracted using the ensemble empirical mode decomposition. Then the correlation between the extracted data and various climate indices was analyzed. As a result, autumn-averaged AMM, autumn-averaged AMO, and summer-averaged NINO4 in the previous year significantly influenced the long-term trend of the annual maximum rainfall data at almost all stations. The selected seasonal climate indices were applied to the generalized extreme value (GEV) model and the best model was selected using the AIC. Using the model diagnosis for the selected model and the nonstationary GEV model with the linear trend, we identified that the selected model could compensate the underestimation of the rainfall quantiles.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.35-41
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• 2014

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in a case-by-case manner. In this study, we used Empirical Mode Decomposition Method from Hilbert-Huang Transform to extract them in a systematic manner with least number of ad-hoc parameters set by users. After the periodic components are removed, the remaining time-series data can be analyzed with traditional methods such as ARIMA model. Then we suggest a different way of setting control chart limits for characteristic data with periodic components in addition to ARIMA components.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4A
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• pp.507-515
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• 2008

In this study, a method predicting the displacement response of structures from the measured dynamic strain signal is proposed by using mode decomposition technique. Evaluation of bridge stability is normally focused on the bridge completed. However, dynamic loadings including wind and seismic loadings could be exerted to the bridge under construction. In order to examine the bridge stability against these dynamic loadings, the prediction of displacement response is very important to evaluate bridge stability. Because it may be not easy for the displacement response to be acquired directly on site, an indirect method to predict the displacement response is needed. Thus, as an alternative for predicting the displacement response indirectly, the conversion of the measured strain signal into the displacement response is suggested, while the measured strain signal can be obtained using fiber optic Bragg-grating (FBG) sensors. As previous studies on the prediction of displacement response by using the FBG sensors, the static displacement has been mainly predicted. For predicting the dynamic displacement, it has been known that the measured strain signal includes higher modes and then the predicted dynamic displacement can be inherently contaminated by broad-band noises. To overcome such problem, a mode decomposition technique was used. Mode decomposition technique estimates the displacement response of each mode with mode shape estimated to use POD from strain signal and with the measured strain signal decomposed into mode by EMD. This is a method estimating the total displacement response combined with the each displacement response about the major mode of the structure. In order to examine the mode decomposition technique suggested in this study model experiment was performed.