• Journal of Korean Academic Society of Home Health Care Nursing
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• v.18 no.2
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• pp.81-87
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• 2011

Purpose: The aim of this study was to estimate optimization model of stay time in EMC. Methods: Data were collected at an EMC in a hospital using medical records from June to August in 2007. The sample size was 8,378. The data were structured by stay time for doctor visit, decision making, and discharge from EMC. Descriptive statistics were used to find out general characteristics of patients. Average mean and quantile regression models were adopted to estimate optimized stay time in EMC. Results: The stay times in EMC were highly skewed and non-normal distributions. Therefore, average mean as an indicator of optimal stay time was not appropriate. The total stay time using conditional quantile regression model was estimated about 110 min, that was about 166 min shorter than estimated time using average mean. Conclusion: According to these results, we recommend to use a conditional quantile regression model to estimate optimal stay time in EMC. We suggest that this results will be used to develop a guideline to manage stay time more effectively in EMC.

• Journal of the Korean Regional Science Association
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• v.31 no.3
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• pp.3-17
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• 2015

In Korea, the private education spending of the households accounted for about 3% of GDP and such a education fever has been associated with the financial burden of households. The main purpose of this paper is to investigate the effects of regional education environment on the private education expenditure of the households using the Korean Labor and Income Panel Survey(KLIPS) data. The quantile regression model is used to examine whether the effects of regional education environment such as the degree of education fever differ across the 'quantiles' in the conditional distribution of private education expenditure. The empirical results showed that the amount of private education expenditure is under the influence of the regions where the households reside. In addition, it was found that the private education spending of the households in the upper quantile groups are more likely to be affected by the regional education environments than those in the lower quantile groups.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1531-1541
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• 2013

This paper proposes a new method of VaR forecasting using the conditional autoregressive VaR (CAViaR) models and information criteria. Instead of using a single CAViaR model, we propose to utilize several candidate CAViaR models during a forecasting period. By adopting the Akaike and Bayesian information criteria for quantile regression, we can update not only parameter estimates but also the CAViaR specifications. We also propose extended CAViaR models with a constant location parameter. An empirical study is provided to examine the performance of the proposed method. The results suggest that our method shows more stable performance than those using a single specification.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.421-433
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• 2017

The CTE (conditional tail expectation) is a useful risk management measure for a diversified investment portfolio that can be generally estimated by using a transformed univariate distribution. Hong et al. (2016) proposed a multivariate CTE based on multivariate quantile vectors, and explored its characteristics for multivariate normal distributions. Since most real financial data is not distributed symmetrically, it is problematic to apply the CTE to normal distributions. In order to obtain a multivariate CTE for various kinds of joint distributions, distribution fitting methods using copula functions are proposed in this work. Among the many copula functions, the Clayton, Frank, and Gumbel functions are considered, and the multivariate CTEs are obtained by using their generator functions and parameters. These CTEs are compared with CTEs obtained using other distribution functions. The characteristics of the multivariate CTEs are discussed, as are the properties of the distribution functions and their corresponding accuracy. Finally, conclusions are derived and presented with illustrative examples.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.444-453
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• 2018

The study addresses the analysis on the financial determinants of corporate research and development (R&D) expenditure in finance. Overall level of R&D spending was estimated as one of the top-tier on a global basis and a majority of the expenditure was invested by large domestic firms in private sector. Consequently, financial factors that influence R&D intensity were empirically tested in the first hypothesis by using conditional quantile regression model for firms listed in KOSPI stock market in the post-era of the global financial turmoil. Firms in the groups of high- and low-R&D intensity were statistically compared to detect financial differences in the second hypothesis which was accompanied by the test of multi-logit model that included firms without R&D outlay. Concerning the results of the hypothesis tests, R&D spending of the prior fiscal year, firm size, business risk and advertising expense overall showed statistically significant impacts to determine the level. As an extended study of [1] that had examined financial factors of R&D intensity at the macro-level, the results of the present study are anticipated to contribute to maximizing shareholders' wealth in advance or emerging capital markets, when applied to find an optimal level of R&D expenditure.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.179-190
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• 2020

This research analyses the financial characteristics of corporate R&D intensity in the Korean capital market. It is important to pay greater attention to this subject, given the current situation of the shortage of core components domestically in Korea. Three hypotheses are postulated to investigate the financial factors of R&D investments for KOSDAQ-listed firms during the post-era of the global financial turmoil. By applying a conditional quantile regression (CQR) model, three variables included R&D intensity in the previous year (Lag_RD), the squared term of Lag_RD, and interaction between the high-tech sector and Lag_Rd, reveal significant effects on the current R&D ratio. Whereas more than half of the total variables show variable impacts between firms with higher and lower R&D intensity, only Lag_RD and squared term of Lag_RD were found to be significant. It is expected that these results may contribute to being financial catalysts for an optimal level of R&D expenditures, thereby maximizing firm value for shareholders in KOSDAQ-listed firms.

• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.973-988
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• 2014

This study investigated one of the contemporary issues in the Korean capital market and two hypotheses of concern were tested on the financial determinants of profitability for the firms belonging to the Korean chaebols during the era of the post-global financial turmoil. The first hypothesis applying conditional quantile regression (CQR) estimation provided the evidence that leverage ratio, fixed asset utilization, and foreign ownership among the nine quantitative explanatory variables, had overall statistical significance relative to the book-valued profitability measure, while additional variables such as a firm's size, fixed and a proxy for the type of exchange market showed their strong impacts on the market-valued profitability indicator. Concerning the formulated 'extended' DuPont system, only two components of EBITDAEBIT and EMULTIPLIER revealed their prominent influence on ROE (Return on Equity) over the two tested periods (the years 2008 and 2012).

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.337-354
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• 2002

This paper derives three estimation methods for the between group variance component for serially correlated random model. To compare their estimation capability, three designs having different degree of unbalancedness are considered. The so-called empirical quantile dispersion graphs(EQDGs) used to compare estimation methods as well as designs. The proposed conditional ANOVA estimation is robust for design unbalancedness, however, ML estimation is preferred to the conditional AOVA and REML estimation regardless of design unbalancedness and correlation coefficient.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.365-375
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• 2022

This study examines the relationship between board gender diversity and financial performance. The annual data of Palestinian nonfinancial listed enterprises from 2015 to 2019 was analyzed using a longitudinal panel analysis for the study's purposes. When conditional mean regression methodologies were used in the study, the results indicate that there is an insignificant relation between board gender diversity and firm financial performance. However, when analyzing women directors' effect on a firm's financial performance, endogeneity is always a concern, therefore, we test for endogeneity by employing the Darbin-Wu Housman test and then by using 2SLS. Nevertheless, when looking at the dispersion of a firm's performance using quantile regression, the results show that having women on the board improves financial performance slightly, especially for high-financial-performing firms. The findings indicate that there is a legal significant gap hindering the protection of gender diversity in boardrooms, and limiting the existence and representation of women in leadership positions, specifically, board of directors. The results of this study contribute to corporate governance and business culture literature by shedding the light on the importance of board gender diversity, to improve the firm financial performance, and hence, protect the interests of all shareholders' categories.

• Environmental and Resource Economics Review
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• v.27 no.1
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• pp.195-214
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• 2018

This study estimates the price elasticity of residential water demand in Korea. For that, annual panel data from the year of 2010 to 2013 for 161 local water services is estimated by using panel quantile model. As a result, the price elasticities of residental water demand in Korea are estimated to be between -0.156 and -0.189 depending on its quantile. In addition, the study finds that the estimated elasticity of residential water demand by traditional conditional mean regression is relatively more influenced by high demand areas because the distribution of residental water demand in Korea is left-skewed.