• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.209-223
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• 2024

Purpose - This article analyzes the impact of macroeconomic variables of the United States, China, and Korea on KOSPI and VKOSPI, in that United States and China have a great influence on Korea, having an export-driven economy. Design/methodology/approach - The influence of US, China, and Korea interest rates, industrial production index, consumer price index, US employment index, Chinese real estate index, and Korea's foreign exchange reserves on KOSPI and VKOSPI is analyzed on monthly basis from Jan 2012 to Aug 2023, using multifactor model. Findings - The KOSPI showed a positive relationship with the U.S. industrial production index and Korea's foreign exchange reserves, and a negative relationship with the U.S. employment index and Chinese real estate index. The VKOSPI showed a positive relationship with the Chinese consumer price index, and a negative relationship with the U.S. interest rates, and Korean foreign exchange reserves. Next, dividing the analysis into two periods with the Covid crisis and the analysis by country, the impact of US macroeconomic variables on KOSPI was greater than Chinese ones and the impact of Chinese macroeconomic variables on VKOSPI was greater than US ones. The result of the forward predictive failure test confirmed that it was appropriate to divide the period into two periods with economic event, the Covid Crisis. After the Covid crisis, the impact of macroeconomic variables on KOSPI and VKOSPI increased. This reflects the financial market co-movements due to governments' policy coordination and central bank liquidity supply to overcome the crisis in the pandemic situation. Research implications or Originality - This study is meaningful in that it analyzed the effects of macroeconomic variables on KOSPI and VKOSPI simultaneously. In addition, the leverage effect can also be confirmed through the relationship between macroeconomic variables and KOSPI and VKOSPI. This article examined the fundamental changes in the Korean and global financial markets following the shock of Corona by applying this research model before and after Covid crisis.

• Journal of Information Technology Services
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• v.9 no.1
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• pp.141-156
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• 2010

Service industry has become the integral part of economy. And the activation of industry requires a scientific and systematic approach. As one of these kinds of efforts, IBM presented a service model which consisted of strategies, processes, technology, manpower and so on through Service Science. And IBM raised the needs of considering the main issue and methodology of service model's scientific approach. On that account, this study worked out factorial definitions and measurable items by the four components of service model presented by Service Science in order for it to be measured through studies on related literature and interviews of specialists. In order to prepare an opportunity of categorizing the conception of the service model of which character was strongly intangible and of systematically approaching to it, the investigator examined related literature and applied it QFD methodology. Through this kind of redefinition, the investigator concretely conceptualized the service model which was standstill at the level of theoretical approach so far and presented guidelines with which researchers could classify it more concretely. In addition, the investigator tried to present a systematic frame which could be applied to business by working-level officials or decision-makers. Therefore, the investigator expects that study findings will be used as references for the design or implementation of service-oriented business model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.458-465
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• 2020

The purpose of this study is to analyze the effect of policy in Jeju, using a macro-economic forecasting model of Jeju. First, the model's reality explanatory power improved by updating its statistics to 2017 and expanding new policy variables and modules. Also, the industrial structure of the model was further subdivided and extended to be considered simultaneously in the demand side of Keynesian theory. Second, it was determined that the predictive power for the model of this study was better than that of the existing model. However, with some endogenous variables, it was possible to identify implications that should be developed and considered when the model is improved with additional data in the future. Third, when the second airport construction was considered, it was observed that its effect was an increase of 1.25 times for GRDP, 1.2 times for employment, 1.48 times for private consumption, and 2.06 times for investment. Also, the economic growth rate was estimated to be 1.6% point higher than when the second airport was not constructed. Finally, the results of this study are expected to be used for policy decision making of the Jeju Government.

• Journal of Navigation and Port Research
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• v.44 no.1
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• pp.44-52
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• 2020

More accurate forecasting of port cargo in the global long-term recession is critical for the implementation of port policy. In this study, the Busan Port container volume (export cargo and transshipment cargo) was estimated using the Vector Autoregressive (VAR) model and the vector error correction (VEC) model considering the causal relationship between the economic scale (GDP) of Korea, China, and the U.S. as well as ARIMA, a single volume model. The measurement data was the monthly volume of container shipments at the Busan port J anuary 2014-August 2019. According to the analysis, the time series of import and export volume was estimated by VAR because it was relatively stable, and transshipment cargo was non-stationary, but it has cointegration relationship (long-term equilibrium) with economic scale, interest rate, and economic fluctuation, so estimated by the VEC model. The estimation results show that ARIMA is superior in the stationary time-series data (local cargo) and transshipment cargo with a trend are more predictable in estimating by the multivariate model, the VEC model. Import-export cargo, in particular, is closely related to the size of our country's economy, and transshipment cargo is closely related to the size of the Chinese and American economies. It also suggests a strategy to increase transshipment cargo as the size of China's economy appears to be closer than that of the U.S.

• Journal of Energy Engineering
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• v.21 no.3
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• pp.281-291
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• 2012

Recently, global economy has recovered and aspects of the renewable energy industry in the global competition is more fierce, the new growth engines of the major countries, including the United States and China, industry promotion policy as being deployed. Major advanced countries and Korea also invested a lot of money to wind power development as a part of renewable energy development and promoting the construction of wind power generation. The global wind power generation market is expected to further increase the scale to about 70 billion US dollars, thus, Korea as well as the installation of domestic wind power overseas actively considering. This study uses input-output analysis to estimate the role of wind power generation sector exports national economy. More specifically, this study shows what national economy effect of production-inducing effect, value-added inducing effect, and employment-inducing effect are explored with demand-driven moel. After define wind power generation sector what small sized of Input-Output table 168 sectors among 11 sectors, this study pays particular and close attention to wind power generation sector by taking the sector as exogenous specification and then investigating economic impacts of it. The wind power generation exportation case of overseas 100 billion won, production-inducing effect, value-added inducing effect, and employment-inducing effect are 205 billion won, 68 billion won and 1,054 persons, respectively. These quantitative information can be usefully utilized in the policy-making for the industrialization of wind power generation exports.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.73-80
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• 2012

Consulting industry is a knowledge intensive business service industry to lead to knowledge creation as well as support high value creation of other industries. While the consulting industry has a great ripple effect on whole industries, there is a lack of interest in the consulting industry. Thus, this study investigates a ripple effect of the consulting service on the national economy using an Input-Output analysis. We summarize some of our findings as follows. First, A production of 1.0 won in the business consulting industry induces production of 0.6933 won in 2005 and 0.7851 in 2009; value-added of 0.2881 won in 2005 and 0.3039 won in 2009. A production of 1.0 billion won in the business consulting industry Industry produces employment for 0.1124 persons in 2005 and 0.1207 persons in 2009. Second, the supply shortage of 1.0 won in the business consulting industry prevents other industries from producing 2.6759 won in 2005 and 3.0145 won in 2009. Third, a 10% increase in the price level of the business consulting industry raises the overall price level from 0.1691% in 2005 to 0.2161% in 2009. The research results show that consulting industry has been increasing a effect on Korea national economy.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.627-644
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• 2009

In the knowledge-based economy highlighting the importance of human capital, there has been a growing interest in human capital externalities as a fundamental engine of growth and development of a region. The purpose of this study is to analyze human capital externalities using 3-level hierarchical linear model(3-HLM), decomposing determinants of wages into three levels involving workers(level-1) nested within firms(level-2) nested within regions(level-3). This study separately estimates the effect of the average education level on the wages by three different schooling groups on the assumption that the intensity of knowledge spillovers varies with each group's schooling level. The main results are as follows; First, the coefficient of the average education level of a region shows 0.044, indicating that one-year increase in the average level of schooling could increase average individual earnings by 4.4%. Secondly, the external effects of human capital on three different schooling groups are considerably different, raising less than high school graduates' wages by 3.0%, college graduates' wages by 4.7%, and graduate schools' wages by 11.8%, respectively. Thirdly, well educated workers are much more sensitive to the variation of the regional education level than less educated ones when we apply the shares of each schooling group as alternative measures for the average level of education. Such findings of this study draw an implication that local governments could speed up regional economic growth in the knowledge-based economy by not only raising total human capital stock in a region but building the close networks that promote productivity-enhancing human capital external effects.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.1
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• pp.129-146
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• 2014

This paper investigates how industrial diversity affects unemployment and employment instability from the perspective of the regional economy. Through this analysis, we examine how the industry-specific policy to promote some industry strategically in most of areas affects the stability of the regional economy. We measure Herfindahl indexes using the 1993-2010 data of 16 regions in Korea, and use panel regression model for empirical analysis. The main results from this empirical analysis are summarized as follows. First, we confirm that the industrial structure of most regions has been changed to the direction of specialization in 1990s and to the direction of diversification in 2000s through analyzing the changes in the values of Herfindahl indexes during the given period. Second, we find from the estimation results of panel regression model that the higher industrial diversity in most of regions is, the lower the unemployment rate is. However, a statistically significant relationship between industrial diversity and employment instability only partially confirmed. Third, there exist high unemployment rate and employment instability in most metropolitan areas, but it is hard to say that this relationship is highly statistically significant. From the results of the empirical analysis, it is likely that the industry-specific policies such as the regional strategic industry development policies unlike policy goals make the unemployment rate to rise and economic instability to increase. From the viewpoint of employment aspects, the strategies to increase industrial diversity would be desirable rather than those to specialize in the industrial structure.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.103-109
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• 2001

Soft sensor is an effective method to deal with the estimation of variables, which are difficult to measure because of the reasons of economy or technology. Fuzzy logic system can be used to develop the soft sensor model by infinite rules, but the fuzzy dividing of variable sets is a key problem to achieve an accurate fuzzy logic model, In this paper, we proposed a new method to develop soft sensor model based on fuzzy neural network. First, using a novel method to divide the variable fuzzy sets by the process input and output data. Second, developing the fuzzy logic model based on that fuzzy set dividing. After that, expressing the fuzzy system with a fuzzy neural network and getting the initial soft sensor model based FNN. Last, adjusting the relative parameters of soft sensor model by the BP learning method. The effectiveness of the method proposed and the preferable generalization ability of soft sensor model built are demonstrated by the simulation.

• Journal of Korean Society for Quality Management
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• v.51 no.3
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• pp.363-379
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• 2023

Purpose: The main objective of this research is to construct an AI-based Composite Supplementary Index (ACSI) model to achieve accurate predictions of the Composite Index of Business Indicators. By incorporating various economic indicators as independent variables, the ACSI model enables the prediction and analysis of both the leading index (CLI) and coincident index (CCI). Methods: This study proposes an AI-based Composite Supplementary Index (ACSI) model that leverages diverse economic indicators as independent variables to forecast leading and coincident economic indicators. To evaluate the model's performance, advanced machine learning techniques including MLP, RNN, LSTM, and GRU were employed. Furthermore, the study explores the potential of employing deep learning models to train the weights associated with the independent variables that constitute the composite supplementary index. Results: The experimental results demonstrate the superior accuracy of the proposed composite supple- mentary index model in predicting leading and coincident economic indicators. Consequently, this model proves to be highly effective in forecasting economic cycles. Conclusion: In conclusion, the developed AI-based Composite Supplementary Index (ACSI) model successfully predicts the Composite Index of Business Indicators. Apart from its utility in management, economics, and investment domains, this model serves as a valuable indicator supporting policy-making and decision-making processes related to the economy.