• The Journal of Economics, Marketing and Management
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• v.12 no.3
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• pp.23-33
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• 2024

This study aims to enhance the accuracy of effective demand analysis for publicly supported private rental housing by integrating the RIR into the traditional Mankiw-Weil (MW) model. Traditional models like the M-W model, which account for household income, housing costs, and household size, often fall short in estimating demand driven by large-scale development projects. By integrating the RIR factor, this study introduces a more accurate and practical approach to analyzing effective housing demand. Findings show that the modified M-W model incorporating RIR predicts effective demand with greater precision than traditional methods. This advancement allows developers to plan projects more efficiently and aids governments and local authorities in implementing more effective housing policies. Furthermore, the study assesses the real housing cost burden on households, elucidating their capacity to pay housing costs based on household size and income quintile. This information enables policymakers to design targeted housing support policies for specific demographic groups. Additionally, the research provides comprehensive policy recommendations tailored to various regions and housing types. Overall, this study lays a vital groundwork for the long-term analysis of the effects of economic changes and housing market trends on effective demand.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1547-1555
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• 2016

The variability of exchange rate influences on the various aspect, especially economics, social phenomenon, industry, and culture of the country. In this article, time series model that won/yen exchange rate can be explained by won/dollar exchange rate has been studied. Daily exchange rate data have been used from January 1, 1999 to December 31, 2015. The daily data divided into two period based on the world financial crisis, September 13, 2008. The first period was January 1, 1999 through September 12, 2008 and the second period was October 1, 2008 through December 31, 2015. The AR+IGARCH (1, 1) model has been used for analyzing the variability of exchange rate. In both first period and second period, the estimation of won/yen exchange rate are somewhat underestimated compared with the actual value.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1471-1479
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• 2017

The variability of trade price index of apartment influences on the various aspect, especially economics, social phenomenon, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly trading price index of apartment data. About 16 years of the monthly data have been used from September 2001 to May 2017. In the ARE model, six macroeconomic variables are used as the explanatory variables for the rade price index of apartment. The six explanatory variables are mortgage rate, oil import price index, consumer price index, KOSPI stock index, GDP, and GNI. The result has shown that trading price index of apartment explained about 76% by the mortgage rate, and KOSPI stock index.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1306-1325
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• 2019

As the world is moving towards digitization, data is generated from various sources at a faster rate. It is getting humungous and is termed as big data. The financial sector is one domain which needs to leverage the big data being generated to identify financial risks, fraudulent activities, and so on. The design of predictive models for such financial big data is imperative for maintaining the health of the country's economics. Financial data has many features such as transaction history, repayment data, purchase data, investment data, and so on. The main problem in predictive algorithm is finding the right subset of representative features from which the predictive model can be constructed for a particular task. This paper proposes a correlation-based method using submodular optimization for selecting the optimum number of features and thereby, reducing the dimensions of the data for faster and better prediction. The important proposition is that the optimal feature subset should contain features having high correlation with the class label, but should not correlate with each other in the subset. Experiments are conducted to understand the effect of the various subsets on different classification algorithms for loan data. The IBM Bluemix BigData platform is used for experimentation along with the Spark notebook. The results indicate that the proposed approach achieves considerable accuracy with optimal subsets in significantly less execution time. The algorithm is also compared with the existing feature selection and extraction algorithms.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.387-397
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• 2015

In this paper, the determinants of the number of job changes in the SMEs (small and medium enterprises) youth-intern project is analysed, utilizing SMEs youth-intern DB and employment insurance DB. Since the number of job changes are count data which take integer values other than negative values, general linear regression analysis becomes inappropriate. Therefore, four models such as Poisson regression model, zero inflated Poisson regression model, negative binomial regression model and zero inflated negative binomial regression model are tried to fit count data. A zero inflated negative binomial regression model is selected to be the best model. Major results are the followings. First, the number of job changes is shown to be significantly smaller in the treatment group than in the control group. Second, the number of job changes turns out to be significantly smaller in the young-age group than in the old-age group. Third, it is also shown that the number of job changes of man is significantly greater than that of woman. Lastly, the number of job changes in the bigger firm is shown to be significantly less than that of the smaller firm.

• Economic Analysis
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• v.27 no.1
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• pp.67-90
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• 2021

This study found an interesting fact that the nonlinear relationship structure between volatility and trading volume changed before and after the COVID-19 pandemic according to empirical analysis using Bitcoin (BTC) market data that sensitively reflects investors' trading behavior. That is, their relationship appeared positive (+) in a stable market state before COVID-19 pandemic, as in theory based on the information flow paradigm. In a state under severe market stress due to COVID-19 pandemic, however, their dependence structure changed and even negative (-). This can be seen as a consequence of increased market stress caused by COVID-19 pandemics from a behavioral economics perspective, resulting in structural changes in the asset market and a significant impact on the nonlinear dependence of volatility and trading volume (in particular, their dependence at extreme quantiles). Hence, it should be recognized that in addition to information flows, psychological phenomena such as behavioral biases or herd behavior, which are closely related to market stress, can be a key in changing their dependence structure. For empirical analysis, this study performs a test of Ross (2015) for detecting a structural change, and proposes a Copula Regression Quantiles (CRQ) approach that can identify their nonlinear relationship structure and the asymmetric dependence in their distribution tails without the assumption of i.i.d. random variable. In addition, it was confirmed that when the relationship between their extreme values was analyzed by linear models, incorrect results could be derived due to model specification errors.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.253-269
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• 2005

One of the basic assumptions of the regression models is that the parameter vector does not vary across sample observations. If the parameter vector is not constant for all observations in the sample, the statistical model is changed and the usual least squares estimators do not yield unbiased, consistent and efficient estimates. This study investigates the regression model with some or all parameters vary across partitions of the whole sample data when the model permits different response coefficients during unusual time periods. Since the usual test for overall homogeneity of regressions across partitions of the sample data does not explicitly identify the break points between the partitions, the testing the equality between subsets of coefficients in two or more linear regressions is generalized and combined with the test procedure to search the break point. The method is applied to find the possibility and the turning point of the structural change in the long-run unemployment rate in the usual static framework by using the regression model. The relationships between the variables included in the model are reexamined in the dynamic framework by using Vector Autoregression.

• International Journal of Human Ecology
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• v.16 no.2
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• pp.79-88
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• 2015

As information and communications technology (ICT) becomes increasingly integrated into the daily lives of people around the world, it is important to know how the technology is influencing the behaviors of individuals and families. This study looked at the ecology of families as it is related to ICT and the changes to processes that occur as ICT devices and services are integrated into the family. A survey of 1084 families was conducted. Five hundred of the families were from the United States and 584 families were from Korea. Significant differences were found in the use of ICT by Korean and American families although the source of this difference was not clearly identified in this study. Three clusters of families were identified based on their use of devices and services. These were labeled as; 'The Tech Savvy', 'The Wireless Users', 'The In-betweeners', 'The Wired', and 'The Just Mobile'. 'The Tech Savvy' used the greatest variety of ICT technologies and 'The Wired' used the fewest. Other clusters fell in the middle with families seemingly using the devices which met their particular needs. Two factors related to ICT integration into the family were identified. These were related to family intimacy and family relationship maintenance. The family cluster identified as 'Tech Savvy' made significantly greater use of ICT in these relationships and 'The Wired' made the least use of ICT in these areas. The other clusters tended to be between the two ends and tended not to be significantly different from each other in their use of ICT. Finally, models for ICT use by families showed that demographics, nation of origin, types of devices and services used, and attitude and interest in ICT all had a significant impact.

• Korean Journal of Agricultural Science
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• v.44 no.3
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• pp.451-461
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• 2017

Korean agriculture has recently focused on the 6th dimension of industrialization, which includes the functions of healing and care. The green care and healing business is one of the most representative models, satisfying modern consumers' needs for care or healing in rural agricultural environments. Many studies have shown physical and social benefits from green care and healing, but studies regarding economic performance are rarely found. The present study aimed to analyze the economic feasibility of different green care and healing farm complexes proposed in recent domestic research, with various possible combinations of business scenarios. The results show that most of the scenarios are economically feasible as B/C (benefit-cost ratio) and IRR (internal rate of return) are 1.19 and 8.53%, respectively, under scenario 1. This study also performed a break-even analysis for providing more flexible decision-making information. Overall, scenario 1 from green care and healing site and scenario 4 from green care and healing cluster are found to be superior to the other scenarios in terms of B/C and IRR. The scenarios in this study reflect the domestic farms or complexes which have similar functions of care or healing. Therefore, the results of this study provide information on practical policies and business implications in making decisions on the specific size and operational patterns when adopting green care and healing complexes by central or local governments and private sectors in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1739-1748
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• 2021

Real estate prices affect countries, businesses, and households, and many studies have been conducted on the real estate bubble in recent soaring real estate prices. However, if the real estate bubble prediction simply compares the real estate price, or if it does not reflect key psychological variables in real estate sales, it can be judged that the accuracy of the bubble prediction model is poor. The purpose of this study is to design a predictive model that can explain the real estate bubble situation by region using the autoencoder technique. Existing real estate bubble analysis studies failed to set various types of variables that affect prices, and most of them were conducted based on linear models. Thus, this study suggests the possibility of introducing techniques and variables that have not been used in existing real estate bubble studies.