• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.428-435
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• 2015

When I examined the corporate financing statistics in Korea, I have recognized that there are several trends of them. First, large enterprises use bank loan and direct financing like corporate bond as debt. Second, small and medium companies mainly use bank loan only. So I argue that there is sample selection bias in corporate debt choice and using sample selection methodology is more adequate when analysing the behavior in corporate debt choice. Therefore I have tested panel sample selection model, using the listed korean firm data from 1990 to 2013 and I have found that the panel sample selection model is appropriate.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1077-1085
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• 2017

In this study, we have found the major factors which affect Korean women's wage analysing the data provided by 2015 Korea Labor Panel Survey (KLIPS). In general, wage data is difficult to analyze because random sampling is infeasible. Heckman sample selection model is the most widely used method for analysing the data with sample selection. Heckman proposed two kinds of selection models: the one is the model with maximum likelihood method and the other is the Heckman two stage model. Heckman two stage model is known to be robust to the normal assumption of bivariate error terms. Recently, Marchenko and Genton (2012) proposed the Heckman selectiont model which generalizes the Heckman two stage model and concluded that Heckman selection-t model is more robust to the error assumptions. Employing the two models, we carried out the analysis of the data and we compared those results.

• Knowledge Management Research
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• v.24 no.4
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• pp.63-85
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• 2023

This study aims to identify the primary user group in the growing metaverse space based on the increased interest during the COVID-19 era. It also aims to explore the predictive factors for metaverse adoption. To predict online activities, the study examined user purposes, motivations, and relevant demographic factors as predictive variables through model analysis. The data from the Korean Media Panel Survey were used, and a two-stage analysis with the Heckman two-stage sample selection model was conducted to predict metaverse users. The analysis revealed that the key factors influencing metaverse adoption were offline activities, openness, OTT usage, and purchasing of paid content. Moreover, in the second stage model, openness, gender, and paid content purchases were identified as significant variables for increasing metaverse usage time. These results indicate that understanding metaverse users is essential in the context of the rising interest in online activities during the COVID-19 era and can provide valuable insights for metaverse platform-related companies and developers.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.647-659
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• 2019

The purpose of this study was to investigate the influence of children's elementary school entrance to working conditions of employed mothers. The data from 4th to 8th wave of Panel Study on Korean Children (PSKC) were used for analysis. Specifically, we examined changes in wages, working hours and regular employment of employed mothers after their children entered elementary schools. We adopted Heck selection model for unbalanced panel data after controlling sample selection bias, and compare results of analysis for unbalanced and balanced panel data. The results showed that children's elementary school entrance reduces employed mothers' wage, working hours and regular employment. These results indicate that mother tend to leave regular job and could not entry into decent job when their children are in elementary school.

• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.437-465
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• 2019

In this study, we propose a new method to forecast long-term energy demand in Korea. Based on Chang et al. (2016), which models the time varying long-run relationship between electricity demand and GDP with a function coefficient panel model, we design several schemes to retain objectivity of the forecasting model. First, we select the bandwidth parameters for the income coefficient based on the out-of-sample forecasting performance. Second, we extend the income coefficient using the functional principal component analysis method. Third, we proposed a method to reflect the elasticity change patterns inherent in Korea. In the empirical analysis part, we forecasts the long-term energy demand in Korea using the proposed method to show that the proposed method generates more stable long term forecasts than the existing methods.