• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.219-233
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• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.6
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• pp.1629-1639
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• 2016

This study is to estimate the production efficiency of abalone aquaculture and to find its determinants utilizing the survey data of operating expenses in 2015. The first part of the analysis applied both DEA and Super-DEA for the estimation of efficiency of each aquaculture household as DMU. We used wages, feeding costs and area as inputs and annual profits and sales as outputs of the model. The second part of the study applied both Tobit and OLS for the identification of determinants of the efficiency. We investigated cost-ratio, depreciation costs, careers, value of living seeds, cleaning costs of farming ground and a ratio of 1 and 2 year-old abalone at shipment as potential determinants. The estimation results show us that the average technical efficiency, pure technical efficiency, and scale efficiency score turn out to be 72%, 81% and 85% respectively. The Super-BCC and Super-CCR models reveal their average efficiency scores as 81% and 80%. All of the variables used to identify the determinants of the efficiency. The study results suggests that the production efficiency can be improved by cleaning farming ground and hence lowering the death rate of seeds.

• Environmental and Resource Economics Review
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• v.24 no.3
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• pp.551-572
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• 2015

Given the presence of global warming, the economic impact of climate changes on output sales has been discussed in the literature, but rarely with empirical evidences. In this present study, a simple log-model was employed to identify the economic impacts of weather changes in manufacturing and service sectors in Korea. For this empirical exercise, weather variables including the CDD (cooling degree days) and HDD (heating degree days) were computed using the Korea's meteorological records covering the period 1970-2012. According to estimation results, 26.7% (144 over 539) and 27.9% (64 over 229) of the manufacturing and service sectors, respectively, are found to be weather-sensitive.

• Environmental and Resource Economics Review
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• v.30 no.4
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• pp.557-579
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• 2021

Collective energy is an intermediate stage in energy conversion and has a great influence on the power structure as a distributed power source. However, the problem of the collective energy business has recently emerged due to the worsening profitability of some collective energy operators. This study measured the technical efficiency of major operators through the estimation of the production efficiency of Korean collective energy operators, and based on this, we looked at ways to improve the profit structure of operators. After collecting detailed data from 16 collective energy operators between 2016 and 2019, the production efficiency of operators was estimated using the panel stochastic frontier model. As a result of the estimation, combined steam power operators showed the highest production efficiency and reverse CHP operators showed the lowest efficiency. Furthermore, as a result of examining the factors influencing profitability, it was confirmed that production efficiency has a positive effect on overall profitability. However, businesses with a high proportion of heat production, such as small district electricity operators, profitability was lower. This phenomenon is due to the structural limitations of the current heat sales market. Hence, the adjustment of the heat sales unit price is necessary to improve profitability of collective energy operators.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.69-79
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• 2022

The purpose of the study is to look into the likelihood of private sector enterprises going bankrupt due to COVID-19 pandemic-related issues. The data for this study was taken from the World Bank's Enterprise Survey, which was intended to assess the impact of the COVID-19 pandemic on the business sector. This study uses the Ordinal Logit Method to analyze the model with dependent variables having ordinal values. The determinants reflect business performance, innovation, business relationships, and government support. According to the estimation results, a lower probability of business closures, illiquidity, and payment delays are found in businesses that maintain sales growth, operating hours, temporary workers, product portfolio, consumer demand, and input supply. Meanwhile, the increase in online business activities and receiving support from financial institutions and the government do not help businesses reduce the risk. Moreover, higher survival is found in manufacturing and developing countries. This implies the fragility of businesses in the retail and service sectors, especially for mega-enterprises in developed countries. In addition, the negative impact of the COVID-19 pandemic on businesses in Europe and West Asia is less severe than in other regions. The results imply policies to support the private sector during the pandemic, such as increasing labor market flexibility or rapidly implementing supportive policies.

• Journal of the Korean Geographical Society
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• v.49 no.6
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• pp.917-934
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• 2014

Geographically weighted regression(GWR) model has been widely used to estimate spatially heterogeneous real estate prices. The GWR model, however, has some limitations of the selection of different price determinants over space and the restricted number of observations for local estimation. Alternatively, the geographically weighted LASSO(GWL) model has been recently introduced and received a growing interest. In this paper, we attempt to explore various local price determinants for the real estate by utilizing the GWL and its applicability to forecasting the real estate price. To do this, we developed the three hedonic models of OLS, GWR, and GWL focusing on the sales price of apartments in Seoul and compared those models in terms of model fit, prediction, and multicollinearity. As a result, local models appeared to be better than the global OLS on the whole, and in particular, the GWL appeared to be more explanatory and predictable than other models. Moreover, the GWL enabled to provide spatially different sets of price determinants which no multicollinearity exists. The GWL helps select the significant sets of independent variables from a high dimensional dataset, and hence will be a useful technique for large and complex spatial big data.

• Journal of Navigation and Port Research
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• v.39 no.1
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• pp.69-75
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• 2015

The concrete purpose of this study is to suggest actually a debt ratio to optimize the capital structure providing a kind of approach to estimate the proper debt ratio with an analytical model and empirical data in Korean shipping industry. The mathematical and analytical model is started from the first equation about ROE, return of net operating income on equity, with an independent variable, debt ratio. It is constructed with several parameters, ROS(return of operating income on sales), TAT(total assets turnover), and NFCL(net finance cost to liabilities). There could not be a certain relationship between debt ratio and ROS or TAT, while some correlation or causality between debt ratio and NFCL. In other words, most of firms with high debt ratio is likely to burden higher finance cost than others with low one. In this case, there is a linearity relationship between debt ratio and NFCL, so then the second equation considering this relation could be included within the analytical approach of this paper. To be short, if the criteria of adequate debt ratio has to be defined as some level of debt ratio to optimize ROE, the ROE could be illustrated as a quadratic equation to debt ratio from two equations. Next, this research estimated those parameters' numbers through the single regression method with data over 12 years of Korean shipping industry, and identified empirically the fact that optimal debt ratio would be approximately 400%. To conclude, if that industry's sales and operating incomes are stable, the debt ratio could be accepted until twice of 200% had forced in order to guarantee its financial safety in past time.

• Environmental and Resource Economics Review
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• v.23 no.2
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• pp.225-248
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• 2014

The purpose of this study is to estimate the optimal tax rate for gasoline in Korea, by utilizing both the parameters for the estimation of the optimal fuel taxes and the theoretical model considering externalities proposed by Parry and Small (2005). The result of simulation shows that the optimum fuel taxes in Korea is calculated in 382 korean won per liter, which is lower than fuel tax rate(529 korean won per liter) currently being imposed. The fuel tax is composed of two types of tax. First is Pigouvian tax caused by negative externality such as traffic congestion and accidents etc. And second is Ramsey tax for optimal commodity sales taxes. We find that Pigouvian tax in Korea is higher than one of U.S. and U.K and Ramsey tax is very small due to the inelastic labor supply comparing to consumption elasticity of fuel. When adjusting the elasticity of labor supply to the UK level, the optimal fuel tax in Korea is very close to the current level of 480 korean won per liter. This paper contributes to suggest the reasonable estimation and discussion in the social optimum fuel tax rates by utilizing the theory and simulation and improve the possibility of the derivation of optimum fuel taxes through both securing the exact parameters and modifying the theoretical model suitable for Korea.

• Journal of the Korea Society for Simulation
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• v.29 no.2
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• pp.1-10
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• 2020

Companies that use open source SW must comply with the "Open Source SW License" in order to use it freely. However, despite the potential legal responsibilities and risks associated with compliance, they do not know or neglect the risks. For this reason, cases of disputes, including license violations, are soaring. Recently, Open source SW license compliance platform services have been developed and actively utilized to address these issues. This study designed a business model for open source SW license compliance platform and conducted an economic feasibility analysis. The focus of the study is the establishment of a business model and the estimation of potential customers and actual purchase rates. For this purpose, we designed seven business model scenarios for promotion and sales period, and performed an economic evaluation using an expanded model of the Bass model, the Kalish model and the customer's maximum willingness to pay.

• Journal of the Korean Geographical Society
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• v.49 no.5
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• pp.761-778
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• 2014

There has been relatively little study to model price for commercial property because of its low transaction volume in the market. Despite of this thin market character, this paper tried to estimate prices for commercial lots as accurate as possible. We constructed a model whose components consist of mean structure(global trend), exponential covariance function and a pure error term, and applied it to actual sales price data of Seoul. We explicitly took account of spatial autocorrelation of land price by utilizing a kriging technique, a representative method of spatial interpolation, because the land price of commercial lots has feature of differential price forming pattern depending on submarkets they belong to. In addition, we chose to apply a bayesian kriging to overcome data scarcity by incorporating experts' knowledge into prior probability distribution. The chosen model's excellent performance was verified by the result from validation data. We confirmed that the excellence of the model is attributed to incorporating both autocorexperts' knowledge and spatial autocorrelation in the model construction. This paper is differentiated from previous studies in the sense that it applied the bayesian kriging technique to estimate price for commercial lots and explicitly combined experts' knowledge with data. It is expected that the result of this paper would provide a useful guide for the circumstances under which property price has to be estimated reliably based on sparse transaction data.