• Journal of Digital Convergence
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• v.16 no.10
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• pp.89-99
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• 2018

The purpose of this study is to analyse the determinants of the change in the welfare expenditure of local governments in 2015. This study analyzed the spatial correlation of welfare expenditure among neighboring local governments and determined the factors affecting the welfare expenditures. According to the results of the study, spatial correlation of welfare expenditure among local governments appears. Determinants, such as socio-economic factors, administrative factors, public financial factors are affecting the amount of the welfare expenditures, but local political factors, and local tax, last year's budgets are not correlated with the amount of local welfare expenditures. In this study, it is significant to found out that the spatial correlation of welfare expenditure among the local governments and to examine the determinants. If possible, it is necessary to analyze the time-series analysis using the multi-year welfare expenditure data, expecially self-welfare expenditures.

• Proceedings of the Korea Association of Information Systems Conference
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• 2000.11a
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• pp.1.2-24
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• 2000

The field of neural networks has been a recent surge in activity as a result of progress in developments of efficient training algorithms. For this reason, and coupled with the widespread availability of powerful personal computer hardware for running simulations of networks, there is increasing focus on the potential benefits this field can offer. The neural network may be viewed as an advanced pattern recognition technique and can be applied in many areas such as financial time series forecasting, medical diagnostic expert system and etc.. The intention of this study is to build and implement one simple artificial neural networks hereinafter called ANN. For this purpose, some literature survey was undertaken to understand the structures and algorithms of ANN theoretically. Based on the review of theories about ANN, the system adopted 3-layer back propagation algorithms as its learning algorithm to simulate one case of medical diagnostic model. The adopted ANN algorithm was performed in PC by using turbo PASCAL and many input parameters such as the numbers of layers, the numbers of nodes, the number of cycles for learning, learning rate and momentum term. The system output more or less successful results which nearly agree with goals we assumed. However, the system has some limitations such as the simplicity of the programming structure and the range of parameters it can dealing with. But, this study is useful for understanding general algorithms and applications of ANN system and can be expanded for further refinement for more complex ANN algorithms.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.161-170
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• 2013

The FX (Foreign Exchange) is a form of exchange for the global decentralized trading of international currencies. The simple sense of Forex is simultaneous purchase and sale of the currency or the exchange of one country's currency for other countries'. We can find the consistent rules of trading by comparing the gradient boosting method and the decision trees methods. Methods such as time series analysis used for the prediction of financial markets have advantage of the long-term forecasting model. On the other hand, it is difficult to reflect the rapidly changing price fluctuations in the short term. Therefore, in this study, gradient boosting method and decision tree method are applied to analyze the short-term data in order to make the rules for the revenue structure of the FX market and evaluated the stability and the prediction of the model.

• Korean Journal of Social Welfare
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• v.42
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• pp.7-40
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• 2000

This paper examines the changes $(1982{\sim}1992)$ of social welfare expenditures of 12 welfare states. This paper focuses on two questions. First, to what extent have there been changes in social welfare expenditure (total social welfare expenditures, income support expenditures, social service expenditures) of 12 welfare states? Second, what are the causes of the changes in social welfare expenditures? Using Comparative Welfare States Data Set by Stephens(1997) and Social Expenditure Database by OECD (1999), this paper attempts to answer two questions. Fuller-Battese model, a data analysis method in pooled cross-sectional time-series analysis, is adopted to identify variables predicting social welfare expenditure changes. This paper analyzes the predictors separately according to the types of welfare states by Esping-Andersen (1990). Predictors are different by the types of welfare states; thus, economic variables such as GDP and financial deficiency have effects on social welfare expenditures of Liberal and Corporatist welfare states. while they have no effects in Social Democratic welfare states. Political variables has effects on social welfare expenditures of Corporatist welfare states, not of Liberal and Social Democratic welfare states. Demographic variables has effects on social welfare expenditures of Social Democratic welfare states rather than Liberal and Corporatist welfare states. This paper provides an additional knowledge about social welfare expenditure changes of 12 welfare states and discusses implications for the development of welfare state in Korea.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1065-1074
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• 2011

During several decades, many researchers in the field of finance have studied Value at Risk (VaR) to measure the market risk. VaR indicates the worst loss over a target horizon such that there is a low, pre-specified probability that the actual loss will be larger (Jorion, 2006, p.106). In this paper, we compare conditional copula method with two conventional VaR forecasting methods based on simple moving average and exponentially weighted moving average for measuring the risk of the portfolio, consisting of two domestic stock indices. Through real data analysis, we conclude that the conditional copula method can improve the accuracy of portfolio VaR forecasting in the presence of high kurtosis and strong correlation in the data.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1449-1466
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• 2014

In this paper, we consider a hedge portfolio based on futures of underlying asset. A classical way to estimate a hedge ratio for a hedge portfolio of a spot and futures is a regression analysis. However, a regression analysis is not capable of reflecting long-run equilibrium between a spot and futures and volatility clustering in the conditional variance of financial time series. In order to overcome such defects, we analyzed KOSPI200 index and futures using VECM-CC-GARCH model and computed a hedge ratio from the estimated conditional covariance-variance matrix. In real data analysis, we compared a regression and VECM-CC-GARCH models in terms of hedge effectiveness based on variance, value at risk and expected shortfall of log-returns of hedge portfolio. The empirical results show that the multivariate GARCH models significantly outperform a regression analysis and improve hedging effectiveness in the period of high volatility.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6315-6324
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• 2015

This study analyses the effects of household finances on rental price of apartment in Seoul which play a major role in real estate policy. We estimate VAR models using time series data. Economy variables such as sales price of apartment in Seoul, consumer price index, hiring rate, real GNI and loan amount of housing mortgage, which relate to household finances and influence the rental price of apartment, are used for estimation. The main findings are as follows. In the short term, the rental price of apartment is impacted by economy variables. Specifically, Relative contributions of variation in rental price of apartment through structural shock of economy variables are most influenced by their own. However, in the long term, household variables are more influential to the rental price of apartment. These results are expected to contribute to establish housing price stabilization policies through understanding the relationship between economy variables and rental price of apartment.

• Journal of Preventive Medicine and Public Health
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• v.52 no.1
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• pp.51-59
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• 2019

Objectives: The purpose of this study was to estimate the mediating effect of subjective unmet healthcare needs on poor health. The mediating effect of unmet needs on health outcomes was estimated. Methods: Cross-sectional research method was used to analyze Korea Health Panel data from 2011 to 2015, investigating the mediating effect for each annual dataset and lagged dependent variables. Results: The magnitude of the effect of low income on poor health and the mediating effect of unmet needs were estimated using age, sex, education level, employment status, healthcare insurance status, disability, and chronic disease as control variables and self-rated health as the dependent variable. The mediating effect of unmet needs due to financial reasons was between 14.7% to 32.9% of the total marginal effect, and 7.2% to 18.7% in lagged model. Conclusions: The fixed-effect logit model demonstrated that the existence of unmet needs raised the likelihood of poor self-rated health. However, only a small proportion of the effects of low income on health was mediated by unmet needs, and the results varied annually. Further studies are necessary to search for ways to explain the varying results in the Korea Health Panel data, as well as to consider a time series analysis of the mediating effect. The results of this study present the clear implication that even though it is crucial to address the unmet needs, but it is not enough to tackle the income related health inequalities.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.751-761
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• 2021

The Vietnamese economy is a developing country that has brought many opportunities and challenges for the banking system. Commercial banks have developed strongly from quality to quantity, which plays a vital role in developing the economy. They play an important role in capital formation, which is essential for the economic development of a country. They provide financial services to the general public and businesses, ensuring economic and social stability and sustainable growth of the economy. Therefore, the relationship between bank depth and economic growth is of importance in research. This paper used a VAR (Vector Autoregressive Models) estimator for time series data models. The data is collected quarterly from the first quarter of the year 2000 to 2020. The study uses the VAR model to examine the causal relationships of economic growth, growth in money supply expansion, private sector capital requirement, and banks' domestic credit. The results indicate a general short-run relationship between banking sector depth and economic growth with a positive connection, but in the long term, the relationship between these variables can be reversed because of other macro factors. The findings show the two-way causal relationship between GDP growth and banking depth factors. This research contributes to policy-making by underlining the banking sector depth determinants when setting regulations and policies to develop the banking sector.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.461-475
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• 2021

Hidden Markov model (HMM) is a statistical model in which the system consists of two elements, hidden states and observable results. HMM has been actively used in various fields, especially for time series data in the financial sector, since it has a variety of mathematical structures. Based on the HMM theory, this research is intended to apply the domestic KOSPI200 stock index as well as the prediction of global stock indexes such as NIKKEI225, HSI, S&P500 and FTSE100. In addition, we would like to compare and examine the differences in results between the HMM and support vector regression (SVR), which is frequently used to predict the stock price, due to recent developments in the artificial intelligence sector.