• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.

• Korean Journal of Construction Engineering and Management
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• v.11 no.5
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• pp.41-52
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• 2010

Even though REITs (Real Estate Investment Trusts) are listed on the stock market, REITs have characteristics that allow them to invest in real estate and financing for real estate development. Therefore REITs is related with stock market and construction business and real estate business. Using time-series analysis, this study analyzed REITs in relation to construction businesses, real estate businesses, and the stock market, and derived influence factor of REITs. We used the VAR (vector auto-regression) and the VECM (vector error correction model) for the time-series analysis. This study classified three steps in the analysis. First, we performed the time-series analysis between REITs and construction KOSPI(The Korea composite stock price index) and the result showed that construction KOSPI influenced REITs. Second, we analyzed the relationship between REITs and construction commencement area of the coincident construction composite index, office index and housing price index in real estate business indexes. REITs and the housing price index influence each other, although there is no causal relationship between them. Third, we analyzed the relationship between REITs and the construction permit area of the leading construction composite index. The construction permit area is influenced by REITs, although there is no causal relationship between these two indexes, REITs influenced the stock market and housing price indexes and the construction permit area of the leading composite index in construction businesses, but exerted a relatively small influence in construction starts coincident with the composite office indexes in this study.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.191-200
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• 2020

This research paper examines the causal relationship between India's economic growth and sectoral contribution to Gross Domestic Product (GDP) and vice versa, in the short-run and long-run, over a 10 years time period. Johansen's method of cointegration is used to study the cointegration between the sectoral contributions to Indian GDP vis-à-vis India's economic growth. Further, the route of interconnection between economic growth and sectoral contribution is tested by using Vector Auto Regression (VAR) model. Special attention was given for investigating impulse responses of economic growth depending on the innovations in sectoral contribution using time-series data from 1960 to 2015. This paper highlighted a dynamic co-relationship among industrial sector contribution and agricultural sector contribution and economic development. In the long run, one percent change in industrial sector contribution causes an increase of 3.42 percent in the economic growth and an increase of 1.12 percent in the primary sector contribution, while in the short run industrial and service sector contributions showed significant impact on economic development and agriculture sector. The changing composition of sector contribution is going to be an important activity for the policymakers to monitor and control where the technology and integration of sectors play a significant role in economic development.

• The Journal of Industrial Distribution & Business
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• v.8 no.1
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• pp.5-13
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• 2017

Purpose - In this article, a dynamic model like a VAR is an appropriate choice for estimating the possible interrelationship between ownership structure and firm performance as a dynamic process. Research design, data, and methodology - Data of this work are collected from Chinese stock exchange including 350 Chinese-listed firms during the period of 1999-2012. We hypothesize that this interrelationship dynamically exists between ownership structure and firm performance. To examine the correlation, a panel Vector Auto-regression (PVAR) approach generated by GMM method is utilized to test the possible dynamic relation embedded in corporate governance. Another two dynamic analysis solutions such as orthogonalized impulse-response function and variance decomposition are also used simultaneously. Results - Findings of this study indicate the evidence that dynamically endogenous relationship exists between ownership structure and firm performance. Further, there is a dynamical correlation between investment and performance. Impulse response and variance decomposition illustrate that impact of a shock to variables themselves is the main source for their variability. Conclusions - The conclusion in this study is that there is a bidirectional and inter-temporal effect between proportion of ownership and corporate performance for a long run in accordance with impulse response function. Overall, our results suggest that corporate governance in China is more market oriented.

• 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.

• The Journal of Society for e-Business Studies
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• v.18 no.4
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• pp.169-193
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• 2013

This study investigates time series data on internet banking systems and business performances for 5 large-scale banks : Kookmin, Woori, Hana, City, Shinhan. These banks have the common features that they merged with other banks around 2000, hence they experienced massive IS integration between banks. This study adopted VAR and VECM for identifying Granger causality between the quality of internet banking systems and the performances of banks(operating revenue and cost). The main results are as follows. First, internet banking system impacts positively on the revenues as well as costs of banks. Second, the improvement of internet banking system is instigated by cost part more than revenue part. Hence, the results imply that banks tries to reduce operating costs via internet banking systems, however the systems rather increased the costs of banks, although the systems increased operating revenues of banks too.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.169-206
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• 2009

This study stems from a question, "How should we understand the pattern of the Korean economy after the 1990s?" Among various analytic methods applicable, this study chooses a Structural Vector Autoregression (SVAR) with long-run restrictions, identifies diverse impacts that gave rise to the current status of the Korean economy, and differentiates relative contributions of those impacts. To that end, SVAR is applied to four economic models; Blanchard and Quah (1989)'s 2-variable model, its 3-variable extensions, and the two other New Keynesian type linear models modified from Stock and Watson (2002). Especially, the latter two models are devised to reflect the recent transitions in the determination of foreign exchange rate (from a fixed rate regime to a flexible rate one) as well as the monetary policy rule (from aggregate targeting to inflation targeting). When organizing the assumed results in the form of impulse response and forecasting error variance decomposition, two common denominators are found as follows. First, changes in the rate of economic growth are mainly attributable to the impact on productivity, and such trend has grown strong since the 2000s, which indicates that Korea's economic growth since the 2000s has been closely associated with its potential growth rate. Second, the magnitude or consistency of impact responses tends to have subsided since the 2000s. Given Korea's high dependence on trade, it is possible that low interest rates, low inflation, steady growth, and the economic emergence of China as a world player have helped secure capital and demand for export and import, which therefore might reduced the impact of each sector on overall economic status. Despite the fact that a diverse mixture of models and impacts has been used for analysis, always two common findings are observed in the result. Therefore, it can be concluded that the decreased rate of economic growth of Korea since 2000 appears to be on the same track as the decrease in Korea's potential growth rate. The contents of this paper are constructed as follows: The second section observes the recent trend of the economic development of Korea and related Korean articles, which might help in clearly defining the scope and analytic methodology of this study. The third section provides an analysis model to be used in this study, which is Structural VAR as mentioned above. Variables used, estimation equations, and identification conditions of impacts are explained. The fourth section reports estimation results derived by the previously introduced model, and the fifth section concludes.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.255-266
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• 2021

Investment demand for factory facilities has increased due to the balloon effect caused by housing price regulation. This study investigated the impact of the real estate market and macroeconomic factors on the bid price ratio of apartment auctions and factory auctions, focusing on the metropolitan area. To this end, we reviewed theories and previous studies on real estate auctions, and examined how macroeconomic variables affect bid price ratio of apartments and factories using the panel vector autoregressive model. It was found that the increase in the apartment bid price ratio increases as the participation in apartment auctions increases. However, as the factory bid price ratio increases, the factory bid price ratio does not increase, it was confirmed that the positive (+) relationship between the successful bid price ratio and the bid price ratioe does not exist, unlike previous studies. Based on the analysis results, it is suggested that the real estate market and macroeconomic factors should be considered for the stable operation of the related relevant auction system. This study has limitations in that it is limited to the metropolitan area. In the future, research that expands the scope of research to the whole country and provinces should be conducted.

• The Journal of Industrial Distribution & Business
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• v.10 no.9
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• pp.53-60
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• 2019

Purpose - The objective of this paper is to discover if there exists a relationship between the economic index and distribution industry index in Korean. Because of the distribution industry boom in the recent years, a lot of interest in the relationship between the economic index and distribution industry index in Korean and the economy has been generated. This article examine on the mutual influence between economic index and distribution industry index in Korean. Research design, data, and methodology - For this purpose, we use the vector-auto regression model, impulse response function and variance decomposition of the economic index and distribution industry index, Granger causality test using weekly data on the economic index and distribution industry price index in korea. The sample period is covering from January 2, 2010 to August 31, 2019. The VAR model can also be linked to cointegration analysis. Cointegration Analysis makes possible to find a mechanism causing x and y to move around a long-run equilibrium (Engle and Granger, 1987). This equilibrium means that external shocks may separate the series temporarily at any particular time, but there will be an overall tendency towards some type of long-run equilibrium. If variables are found to have this tendency they are said to be cointegrated and a long-run relationship between these series is established. These econometric tools have been applied widely into economics and business areas to analyze intertemporal linkages between different time series. Results - This research showed following main results. First, from the basic statistic analysis of the economic index and distribution industry index in Korean, the economic index and the distribution industry index in korea have unit roots. Second, there is at least one cointegration between the economic index and distribution industry index in Korean. Finally, the correlation between of the economic index and the distribution industry index in korea is (+) 0.528876. Conclusions - We find that the distribution industry price index Granger cause the economic index in korea. As a consequence, the distribution industry index affect the economic index in Korean. The distribution industry index to the economic index is stronger than that from the economic index to the distribution industry index.