• Journal of the Korean Regional Science Association
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• v.38 no.4
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• pp.75-91
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• 2022

With the recent increase in interest in climate change issues, the use of bicycles is complementing public transportation and attracting attention as one of the eco-friendly means of transportation. Daejeon Metropolitan City has been operating Tashu, a public bicycle, since 2008. This study empirically analyzed the spatial characteristics that affect the use of public bicycles by grasping the current status and characteristics of public bicycles and applying spatial econometrics analysis, an analysis model that considers the spatial dependence of spatial data. In addition, a comparative analysis was performed by deriving the results of analyzing six models in terms of rental, return, peak time, non-peak time, weekday, and weekend based on the spatial error model identified as the optimal spatial econometrics model. The analysis model results showed that significant spatial characteristics differed according to the type of public bicycle use. In general, the use of public bicycles was high in areas with a high proportion of young people, a high number of public transportation users, good access to universities and rivers, and relatively low land use mix, and high proportion of apartments. These results indicated that public bicycles are used for commuting purposes on weekdays and leisure purposes on weekends, and if the convenience of using bicycles is improved, the use of public bicycles can be further increased.

• Health Policy and Management
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• v.24 no.1
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• pp.13-23
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• 2014

Background: A need arises to efficiently control health expenditure for medical aid due to a sharp increase in medical aid expenditure. This study experimently analyzes the impact of physician behavior on medical use for medical aid beneficiaries using supplier induced demand (SID) theory. Methods: This study looks into analyze SID effect using expenditure factor analysis of medical aid for the years between 2003 and 2010 in comparison with health insurance. Moreover, this study analyzes the existence and scale of SID using econometrics modeling with panel data on 16 cities and provinces's health expenditure data for medical aid from 2003 1/4 to 2010 4/4. Results: This study finds that the growth rate of visit days per capita and treatment amount per visit days for medical aid is higher than health insurance. Furthermore, the result of econometrics modeling analysis shows the existence of SID in general hospital, hospital, clinic, oriental clinic. Conclusion: In order to efficiently control expenditure for medical aid, it is required to reinforce macro polices such as the introduction of 'target management' and micro policies such as the strengthen of management on medical institutes in the perspective of suppliers as well as regulations of demanders.

• 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.

• 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.

• 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.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.11
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• pp.604-609
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• 2004

This paper suggests a long-term distribution area load forecasting algorithm which offers basic data for distribution planning of power system. To build forecasting model, 4-level hierarchical spatial structure is introduced: System, Region, Area, and Substation. And, each spatial load can be decided proportional to its portion in the higher level. This paper introduces the horizon year loads to improve the forecasting results. And, this paper also introduces an effective load transfer algorithm to improve forecasting stability in case of new or stopped substations. The proposed model is applied to the load forecasting of KEPCO system composed of 16 regions, 85 areas and 761 substations, and the results are compared with those of econometrics model to verify its validity.

• Ocean and Polar Research
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• v.34 no.2
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• pp.185-195
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• 2012

This paper focused on forecasting a short-term production of oysters, which have been farmed in Korea, with distinct periodicity of production by year, and different production level by month. To forecast a short-term oyster production, this paper uses monthly data (260 observations) from January 1990 to August 2011, and also adopts several econometrics methods, such as Multiple Regression Analysis Model (MRAM), Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Vector Error Correction Model (VECM). As a result, first, the amount of short-term oyster production forecasted by the multiple regression analysis model was 1,337 ton with prediction error of 246 ton. Secondly, the amount of oyster production of the SARIMA I and II models was forecasted as 12,423 ton and 12,442 ton with prediction error of 11,404 ton and 11,423 ton, respectively. Thirdly, the amount of oyster production based on the VECM was estimated as 10,425 ton with prediction errors of 9,406 ton. In conclusion, based on Theil inequality coefficient criterion, short-term prediction of oyster by the VECM exhibited a better fit than ones by the SARIMA I and II models and Multiple Regression Analysis Model.