• Journal of the Korean Regional Science Association
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• v.31 no.4
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• pp.3-24
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• 2015

Crimes must be reduced not only because of the financial, physical, and emotional damages they bring to the victims but also because crimes increase social costs by elevating distrust in society and instilling fear. With the increasing number of crimes in Korea, finding other factors that affect the occurrence of crimes is needed beyond the current viewpoint for crime analysis. Social support factors can be candidates for studies on the social support effect on crime occurrence in their initial stage. In this study, we identified the effect of social support factors on crime occurrence or deterrence, none of which has been considered important until now, given the emergence of spatial econometrics. The resulting Moran's I values revealed the existence of a spatial autocorrelation in all three crimes: heinous crimes, theft, and violence. As shown in the analysis using spatial econometrics and ordinary least squares, social support from families is significant in reducing all crimes especially violence. Social support from the local government is significant in preventing only theft. The spatial econometrics model is only valid in heinous crimes. These different effects of social support factors and spatial factors on crime occurrences are caused by the different characteristics of crimes. Hence, policymakers should consider the social support effect when they establish policies related to social housing or welfare.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.461-471
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• 2012

The purpose of this paper is to visualize the housing market dynamics with ESDA (Exploratory Spatial Data Analysis) using MATLAB toolbox, in terms of the modeling housing market dynamics in the Busan Metropolitan City. The data are used the real housing price transaction records in Busan from the first quarter of 2006 to the second quarter of 2009. Hedonic house price model, which is not reflecting spatial autocorrelation, has been a powerful tool in understanding housing market dynamics in urban housing economics. This study considers spatial autocorrelation in order to improve the traditional hedonic model which is based on OLS(Ordinary Least Squares) method. The study is, also, investigated the comparison in terms of $R^2$, Sigma Square(${\sigma}^2$), Likelihood(LR) among spatial econometrics models such as SAR(Spatial Autoregressive Models), SEM(Spatial Errors Models), and SAC(General Spatial Models). The major finding of the study is that the SAR, SEM, SAC are far better than the traditional OLS model, considering the various indicators. In addition, the SEM and the SAC are superior to the SAR.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.113-119
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• 1991

The author is currently assistant professor of Management Science at Korea Advanced Institute of Science and Technology, following a few years as assistant professor of Industrial Engineering at Kyung Hee University, Korea. He received his doctorate from the department of Industrial Engineering and Operations Research, University of California, Berkeley. His research interests are time series and forecasting modelling, Bayesian forecasting and the related software development. He is now teaching time series analysis and econometrics at the graduate level.

• Journal of Digital Convergence
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• v.21 no.2
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• pp.9-16
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• 2023

The purpose of this study is to analyze research trends on ESG (Environmental, Social, and Governance), and to present a direction for companies and investors to use ESG information. To this end, text mining, one of the atypical data mining techniques, was used for analysis. Thesis abstracts from January 2014 to February 2023 were collected from the SCOPUS database, and Economics, Econometrics and Finance were the most common. The United States and China published the most ESG papers, and Korea published the 6th most papers in the world. This study is meaningful in that it analyzed the main research trends of ESG using text mining techniques such as LDA and topic modeling. It was confirmed that ESG is being conducted in various fields, not in a specific field, and it is differentiated from previous studies in that it analyzed various influencing factors and ripple effects of ESG.

• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.493-505
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• 1997

The analysis of binary data appears to many areas such as statistics, biometrics and econometrics. In many cases, data are often collected in which some observations are incomplete. Assume that the missing covariates are missing at random and the responses are completely observed. A method to Bayesian analysis of the binary regression model with incomplete data is presented. In particular, the desired marginal posterior moments of regression parameter are obtained using Meterpolis algorithm (Metropolis et al. 1953) within Gibbs sampler (Gelfand and Smith, 1990). Also, we compare logit model with probit model using Bayes factor which is approximated by importance sampling method. One example is presented.

• Environmental and Resource Economics Review
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• v.30 no.3
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• pp.435-469
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• 2021

The main purpose of this study is to identify the determinants of electricity import and export in 26 European Union countries using the Spatial durbin model(SDM). In particular, we would like to mainly explain it based on the amount of power generated by each energy source. Not just the usual way of constructing a weighting matrix based on contiguity, we adopt a weighting method based on the proportion of trade among countries with connected electricity systems. Moreover, the electricity systems of European countries are directly and indirectly connected, which is reflected in the weighting matrix. According to the results, nuclear power has a positive effect on exports and a negative effect on imports, and an increase in wind and solar power has a positive effect on both exports and imports by increasing power system instability. While Korea is unable to trade electricity due to geopolitical conditions, the results of this study are expected to provide implications for energy policies.

• Korea Trade Review
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• v.44 no.2
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• pp.51-62
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• 2019

This study predicts the future world seaborne trade volume with econometrics methods using 23-year time series data provided by Clarksons. For this purpose, this study uses simple regression analysis, exponential smoothing method and seemingly unrelated regression model (SUR Model). This study is meaningful in that it predicts worldwide total seaborne trade volume and seaborne traffic in four major items (container, bulk, crude oil, and LNG) from 2019 to 2023 as there are few prior studies that predict future seaborne traffic using recent data. It is expected that more useful references can be provided to trade related workers if the analysis period was increased and additional variables could be included in future studies.

• Journal of the Economic Geographical Society of Korea
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• v.10 no.4
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• pp.394-413
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• 2007

This paper attempts to analyze spatial determinants of innovative activities at the municipal level in Korea, capitalizing upon spatial econometric techniques. Several spatially weighted matrices will be employed, implying diverse spatial conceptions and interactions. A contribution can be have been made to enhancing an understanding of the spatial interaction and structure of knowledge spillovers in Korea.

• The Journal of Society for e-Business Studies
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• v.4 no.2
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• pp.41-58
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• 1999

This paper is a study on a model of economic value of transmission speed of Internet Commerce. For this research, this paper searches the factors that influence the transmission speed of Internet and suggests the model for measurement of economic value. The model adopted in this research is CVM(contingent valuation method) using in environment economics and the research area in this paper is concentrated on Internet-based Electronic Commerce. For this purpose, this paper suggests econometrics model that measures customer's payment intention for transmission speed of Internet. This model can be used as the basic tool of feasibility of investment analysis and reasonable pricing on Internet service. For the more, it will be followed empirical study and more careful comprehension for objective validity of this study.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.315-327
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• 2021

The Spatial Autoregressive (SAR) models have drawn considerable attention in recent econometrics literature because of their capability to model the spatial spill overs in a feasible way. While considering the Bayesian analysis of these models, one may face the problem of lack of robustness with respect to underlying prior assumptions. The generalized Bayes estimators provide a viable alternative to incorporate prior belief and are more robust with respect to underlying prior assumptions. The present paper considers the SAR model with a set of linear restrictions binding the regression coefficients and derives restricted generalized Bayes estimator for the coefficients vector. The minimaxity of the restricted generalized Bayes estimator has been established. Using a simulation study, it has been demonstrated that the estimator dominates the restricted least squares as well as restricted Stein rule estimators.