• The Korean Journal of Financial Management
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• v.26 no.1
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• pp.127-163
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• 2009

This paper examines the results of survey that the foreign ownership is cointegrated with capital market conditions in Korea using Vector Error Correction Model (VECM) and how the mechanism of innovations and dynamics among the foreign ownership and capital market proxies in the VECM was described. Specifically, we find that the foreign ownership and capital market proxies follow I (1) process and there are cointegrated relations between the foreign ownership and capital market proxies. Adopting the impulse response function and variance decomposition in the VECM, we suggest, in turn, the default risk premia, liquidity of market and the rate of interest in long term business cycle take on a special function on the KSE and KOSDAQ. Finally, we also offer evidences of which there are differences of the mechanism of dynamics and innovations between on the KSE and on the KOSDAQ.

• Management & Information Systems Review
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• v.27
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• pp.131-147
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• 2008

The study examines common component existing in five Asian countries from 1991 to 2007. To do this, the daily stock market indices of Korea, Malaysia, Thailand, Indonesia, and the Philippines were used. Using a Vector Autoregressive Model this paper analyzes causal relations and dynamic interactions between five Asian stock markets. The findings in this study indicate that level of five Asian stock markets' stock return linkages are low. First, from the statistics for pair-wise Granger causality tests, I find Granger-causal relationship between Korea and Indonesia and between Malaysia and and Indonesia. Second, from the results of response function and the statistics of variance decomposition, I find that week shocks to Korean stock market return on Malaysia, Indonesia, Thailand, and the Philippines stock market returns. The results indicate increased Asian stock market linkages but the level is very low. This implies that the benefits of diversification within the five Asian stock markets are still existed.

• Journal of Korea Trade
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• v.24 no.7
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• pp.93-106
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• 2020

Purpose - This paper aims to examine the environmental effects of South Korean foreign trade, and the changing relationship between industrial "three wastes" emissions and foreign trade. Design/methodology - Based on time series data of South Korean foreign trade and industrial "three wastes" from 2009 to 2019, a VAR model was used to analyze the long-term internal links and dynamic changes between foreign trade and environmental pollution. Findings - Variance decomposition analysis shows that for the three types of pollutants, self-impact contributes the most to the variance decomposition. It follows that South Korean foreign trade has a certain negative impact on the environment, and this impact has a certain sustainability. Originality/value - This paper contributes to the study on the relationship between foreign trade and environmental pollution. It theoretically proposes a coordinated development path for foreign trade development and green development based on the environmental impact of foreign trade, to provide a reference for the development of collaborative promotion.

• Nuclear Engineering and Technology
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• v.52 no.2
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• pp.287-295
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• 2020

Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.837-842
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• 2008

Students taking linear model courses have difficulty in determining which parametric functions are estimable when the design matrix of a linear model is rank deficient. In this note a special form of estimable functions is presented with a linear combination of some orthogonal estimable functions. Here, the orthogonality means the least squares estimators of the estimable functions are uncorrelated and have the same variance. The number of the orthogonal estimable functions composing the special form is equal to the rank of the design matrix. The orthogonal estimable functions can be easily obtained through the singular value decomposition of the design matrix.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.189-212
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• 2005

This study examines the interactions between KTB spot and futures markets using the daily prices from March 4, 2002 to January 31, 2005. We use Granger causality test, impulse Response Analysis and Variance Decomposition through vector autoregressive analysis (VAR). However, considering the long-term relationships between the level variables of KTB spot and futures, we introduced Vector Error Correction Model. The main results are as follows. According to the results of Granger-causality test and impulse response analysis, we find that the yields of KTB forward have a great influence on the change of KTB spot but not vice versa. In terms of volatility analysis, there is no inter-dependence between KTB forward and spot markets. In the variance decomposition analysis we find that the short-term KTB forward has much more impact on the KTB spot market than the long-term KTB forward does. We think these results are meaningful for bond investors who are in charge of capital asset pricing valuation, risk management and international portfolio management.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.523-533
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• 2020

This paper suggests three available methods for finding nonnegative estimates of variance components of the random effects in mixed models. The three proposed methods based on the concepts of projections are called projection method I, II, and III. Each method derives sums of squares uniquely based on its own method of projections. All the sums of squares in quadratic forms are calculated as the squared lengths of projections of an observation vector; therefore, there is discussion on the decomposition of the observation vector into the sum of orthogonal projections for establishing a projection model. The projection model in matrix form is constructed by ascertaining the orthogonal projections defined on vector subspaces. Nonnegative estimates are then obtained by the projection model where all the coefficient matrices of the effects in the model are orthogonal to each other. Each method provides its own system of linear equations in a different way for the estimation of variance components; however, the estimates are given as the same regardless of the methods, whichever is used. Hartley's synthesis is used as a method for finding the coefficients of variance components.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.10
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• pp.1109-1114
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• 2012

The performance of a system can be affected by various variables such as manufacturing tolerances, uncertainties of material properties, and environmental factors acting on the system. Robust design optimization has attracted much attention in the design of products because it can find the best design solution that minimizes the variance of the response while considering the distribution of the variables. However, the computational cost and accuracy of optimization have thus far been a challenging problem. In this study, robust design optimization using the multiplicative decomposition method is proposed in order to solve these problems. Because the proposed method calculates the mean and variance of the system directly from the kriging metamodel using the multiplicative decomposition method, it can be used to search for a robust optimum design accurately and efficiently. Several mathematical and engineering examples are used to demonstrate the feasibility of the proposed method.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.607-612
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• 2021

This study estimated the impact of the shock of housing price increase on the total fertility rate and the contribution of each variable to changes in the TFR. This study is differentiated by estimating the contribution rate of each variable to the fertility rate through the Shapley decomposition and the panel VAR's forecast error variance decomposition, which previous studies have not attempted. The main results of this study are as follows. First, the decline in the TFR in Korea has been strongly influenced by the recent decline in the total fertility rate, and this influence is expected to continue in the future. In the case of housing costs, in the past, housing sales prices had a relatively small contribution to changes in the total fertility rate compared to the jeonse prices, but their influence is expected to increase in the long term in the future. It has been demonstrated that private education expenses other than housing sale price and Jeonse price also acted as a major cause of the decline in the total fertility rate.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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• pp.111-124
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• 1999

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.