• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.2
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• pp.19-35
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• 2010

Many papers have used a two-stage approach of first calculating DEA efficiency scores and then seeking to correlate these scores with various environmental variables. Most of the studies have not checked whether such a two-stage approach is statistically valid for identifying significant environmental variables. Recently Simar and Wilson (2007) (SW) introduce a sensible data generating process and bootstrap procedure based on truncated regression for the two-stage approach. Banker and Natarajan (2008) (BN) provide a statistical foundation for the two-stage approach comprising a DEA followed by an ordinary least squares or maximum likelihood estimation. Researchers have to identify an approach suitable for their research circumstances in terms of properties, merits, demerits, and robustness to plausible departures from its chosen data generating process. We summarize the foundations and properties of the two-stage procedures suggested by SW and BN. And we discuss merits and demerits of those procedures. Also using Monte Carlo simulation we assess their relative performance under several misspecified settings.

• Korean Management Science Review
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• v.11 no.2
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• pp.133-146
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• 1994

This study compares the performance of neural networks and ordinary least squares regression with quality-control processes. We examine the applicability of neural networks because they do not require any assumptions regarding either the functional from of the underlying process or the distribution of errors. The coefficient of determination($R^2$), mean absolute deviation(MAD), and the mean squared error(MSE) metrics indicate that neural networks are a viable and can be a superior technique. We also demonstrate that an assessment of the magnitude of the neural notwork input layer cumulative weights can be used to determine the relative importance of predictor variables.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.615-627
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• 2022

Principal Fitted Component (PFC) is a semi-parametric sufficient dimension reduction (SDR) method, which is originally proposed in Cook (2007). According to Cook (2007), the PFC has a connection with other usual non-parametric SDR methods. The connection is limited to sliced inverse regression (Li, 1991) and ordinary least squares. Since there is no direct comparison between the two approaches in various forward regressions up to date, a practical guidance between the two approaches is necessary for usual statistical practitioners. To fill this practical necessity, in this paper, we newly derive a connection of the PFC to covariance methods (Yin and Cook, 2002), which is one of the most popular SDR methods. Also, intensive numerical studies have done closely to examine and compare the estimation performances of the semi- and non-parametric SDR methods for various forward regressions. The founding from the numerical studies are confirmed in a real data example.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1457-1469
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• 2017

Private education expenses is one of the key issues in Korea and there have been many discussions about it. Academically, most of previous researches for private education expenses have used multiple regression linear model based on ordinary least squares (OLS) method. However, if the data do not satisfy the basic assumptions of the OLS method such as the normality and homoscedasticity, there is a problem with the reliability of estimations of parameters. In this case, quantile regression model is preferred to OLS model since it does not depend on the assumptions of nonnormality and heteroscedasticity for the data. In the present study, the data from a survey on private education expenses, conducted by Statistics Korea in 2015 has been analyzed for investigation of the impacting factors for private education expenses. Since the data do not satisfy the OLS assumptions, quantile regression model has been employed in Bayesian approach by using gibbs sampling method. The analysis results show that the gender of the student, parent's age, and the time and cost of participating after school are not significant. Household income is positively significant in proportion to the same size for all levels (quantiles) of private education expenses. Spending on private education in Seoul is higher than other regions and the regional difference grows as private education expenditure increases. Total time for private education and student's achievement have positive effect on the lower quantiles than the higher quantiles. Education level of father is positively significant for midium-high quantiles only, but education level of mother is for all but low quantiles. Participating after school is positively significant for the lower quantiles but EBS textbook cost is positively significant for the higher quantiles.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.611-620
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• 2016

Firms grow in a competitive environment and competition can be a source of corporate growth. In an increasingly global market, companies face increased competition. As such, it is natural that all firms face some degree of risk due to competition. While firms compete for market share, they also imitate competitors in order to minimize risk that accompanies competition. This research attempts to demonstrate the effects of inter-firm competition on investment decisions. Using idiosyncratic equity returns as the instrument variable, this paper uses a two-stage least squares regression, as well as an ordinary least squares (OLS), to identify the influence of peer firms' investment decisions on a firm's own investment strategy. The results confirm that firms show stronger imitative behavior with more intense competition. Also, firms with higher debt ratios show higher peer group influence. This imitative factor provides clues to measure the risk-averseness in investment decisions.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.2
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• pp.15-24
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• 2018

The study examines the magnitude of economic spillover and the impact of foreign direct investment (FDI) inflows on the efficiency of the bank industry in China. This study employs unit root tests, cointegration tests and cointegrating regression analysis, including fully modified ordinary least squares (FMOLS), canonical cointegrating regression (CCR) and dynamic OLS (DOLS) to test the proposed hypotheses. The sample is restricted to the period of time in which monthly data is available and comparable among variables for the period from January 2002 to October 2013 (142 observations). All of the time series data was collected and retrieved from the People's Bank of China, China Monthly Statistics from the National Bureau of Statistics of China, and International Financial Statistics database from International Monetary Fund. The results of the Johansen cointegration test suggest that there is a long-run equilibrium relationship between FDI inflows, foreign exchange rate and banks performance in China. The results of cointegrating regression analysis using FMOLS, CCR and DOLS suggest that M2 supply and FDI inflows are significant at the 0.01 level. The results confirm that FDI inflows in the banking sector are positively related to the increase of banks productivity and performance and short-term loans in China. However, the results suggest that Chinese Yuan currency exchange rate to U.S. dollar is not significant in the banking and financial industry of China.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.139-147
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• 2020

This paper aims to explore the relationship between the quality of the audit and the level of stock return co-movement in the context of the Vietnamese emerging market. The empirical study is designed based on the quatitative method and deductive approach. The panel dataset includes 256 listed firms from different industries,with 1115 firm-year observations on Ho Chi Minh City Stock Exchange for the period from 2014 to 2018. In the research, we built the econometric regression model, using stock return synchronicity and audit quality as the dependent and independent variable, respectively. Some control variables are also added to the econometric regression models as they are well-documented in prior research to have an effect on stock price synchronicity. To improve the accuracy of the regression coefficients, beside the Ordinary Least Squares, we employ the Random Effects Model and the Fixed Effects Model for better statistical analysis of panel data set. The results show that the quality of the audit is positively correlated to stock price synchronicity. This finding suggests that stock returns of companies with higher quality of the audit are more synchronous with the market. Results for other control variables also support our reasoning for the main findings.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.1237-1246
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• 2021

The purpose of the research is to evaluate how education influences the income of household heads, who are young adult in rural Vietnam. In order to examine the impact of education on the households where their heads are young adults, in this paper, the authors employ two research methods. First, ordinary least squares (OLS) regression is used to study the impact of education on different groups of income; second, quantile regression is applied to find out how education influences the income of households. The dataset includes a survey of 800 young households aged between18 and 35 who are the head of agricultural farms in rural areas. The findings indicate that education has a positive impact on income of young households. Furthermore, the results prove that the longer schooling years, the higher income youth can attain. The results showed that, at the survey time (Sep 2019), the average monthly income of rural young adults who are joining the production process shows a big gap between low and high incomes. Moreover, the study has revealed that other factors positively affect the incomes, namely, joining job-related associations, land resource, hired labour, hi-tech application as well as extension of producing unit.

• New & Renewable Energy
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• v.17 no.3
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• pp.24-31
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• 2021

Through 'The Renewable Energy 3020 Implementation Plan' for carbon neutrality, the government promised to raise the proportion of renewable energy generation to 20% and renewable energy installation capacity to 63.8% by 2030. Therefore, we plan to test a 5.5 MW offshore wind turbine near Maldo, Gunsan. In this project, we measure the level of public acceptance and perform ordinary least squares (OLS) regression analysis to show the determinants of public acceptance. The regression results are as followed. First, it is judged that the closer the distance to the offshore wind turbine, the more the economic effects considered by residents. Second, especially in Maldo, the experience of being discriminated from the Saemangeum project, is understood to have caused distrust in the surrounding fishing villages chief/Fisheries Cooperatives, converted into a local community effect. Finally, the policy implications are as follows. First, a bottom-up problem-solving method is required to improve public acceptance, based on the Living Lab. Second, the island community may be indifferent to the briefings or forums of outsiders. Therefore, a gradual approach is required through (in)formal channels based on reliability from a long-term perspective with nearby universities and research institutes using SamsØ Energy Academy.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1197-1211
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• 2011

The actual power performance of historical structural change tests are compared under various alternatives. The tests of interest are F, CUSUM, MOSUM, Moving Estimates and empirical distribution function tests with both recursive and ordinary least-squares residuals. Our comparison of the structural tests involves limiting distributions under the hypothesis, the ability to detect the alternative hypotheses under one or double structural change, and smooth change in parameters. Even though no version is uniformly superior to the other, the knowledge about the properties of those tests and connections between these tests can be used in practical structural change tests and in further research on other change tests.