• KDI Journal of Economic Policy
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• v.36 no.1
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• pp.1-42
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• 2014

This study analyzes how heterogeneous across firms' productivity level the effects of foreign direct investment (FDI) on the productivity of firms in a host country are. The study uses firm level data over 2000~2009 in South Korea and takes a quantile regression approach to estimate FDI's heterogeneous effects on the invested firm ('direct effects') and other domestic firms in the industry to which the invested firm belongs ('intra-industry spillover effects'). Major empirical results are as follows. In manufacturing sector, FDI has positive and statistically significant direct effects on the invested firm. In addition, the higher the quantiles of firms' productivity level are, the larger the positive productivity effects are. FDI also has positive and statistically significant intra-industry spillover effects on domestic firms in low quantiles of productivity while it has negative and statistically significant or insignificant spillover effects on those in high productivity quantiles. In service sector, on the other hand, Sufficient evidence is not found that FDI has statistically significant direct effects or intra-industry spillover effects. Taken together, the study suggests that FDI has heterogeneous effects on the productivity of firms in host country, depending on the firms' productivity level and sector.

• 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.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.791-801
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• 2009

Value-at-Risk(VaR) has been used as an important tool to measure the market risk. However, the selection of the VaR models is controversial. This paper proposes VaR forecast combinations using support vector machine quantile regression instead of selecting a single model out of historical simulation and GARCH.

• 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.

• International Journal of Reliability and Applications
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• v.9 no.1
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• pp.53-70
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• 2008

In this paper, we discuss the plethora of uses for the software package R, and focus specifically on its helpful applications in reliability data analyses. Examples are presented; including the R coding protocol, R code, and plots for various statistical as well as reliability analyses. We explore Kaplan-Meier estimates and maximum likelihood estimation for distributions including the Weibull. Finally, we discuss future applications of R, and usages of quantile regression in reliability.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1537-1545
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• 2015

Support vector quantile regression (SVQR) can be obtained by applying support vector machine with a check function instead of an e-insensitive loss function into the quantile regression, which still requires to solve a quadratic program (QP) problem which is time and memory expensive. In this paper we propose an SVQR whose objective function is composed of an asymmetric quadratic loss function. The proposed method overcomes the weak point of the SVQR with the check function. We use the iterative procedure to solve the objective problem. Furthermore, we introduce the generalized cross validation function to select the hyper-parameters which affect the performance of SVQR. Experimental results are then presented, which illustrate the performance of proposed SVQR.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.173-182
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• 1994

By applying slope estimator under the arbitrary error distributions proposed by Han(1993), if we define regression quantiles to give upper and lower trimming part and blocks of data, we show the proposed slope estimator has asymptotically efficient slope estimator when the number of regression quantiles to from blocks of data goes to sufficiently large.

• Journal of the Korea Society for Simulation
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• v.9 no.3
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• pp.43-51
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• 2000

In regression model, we estimate the unknown parameters by using various methods. There are the least squares method which is the most general, the least absolute deviation method, the regression quantile method and the asymmetric least squares method. In this paper, we will compare each others with two cases: firstly the theoretical comparison in the asymptotic sense and then the practical comparison using Monte Carlo simulation for a small sample size.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.11a
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• pp.6-10
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• 2000

In regression model we estimate the unknown parameters using various methods. There are the least squares method which is the most general, the least absolute deviation, the regression quantile and the asymmetric least squares method. In this paper, we will compare each others with two case: to begin with the theoretical comparison in the asymptotic sense, and then the practical comparison using Monte Carlo simulation for a small sample size.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.1036-1045
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• 2013

The purpose of this study was to examine the effects of Simulation as a Problem-Based Learning (S-PBL) on critical thinking disposition, self-efficacy, and learning attitude and to compare an ordinary least square and a quantile regression method in impacting factors on critical thinking disposition. 143 students from six classes were randomly selected from a total of ten fundamental classes were assigned 66 in the control group and 77 in the experimental group. The results were that the experimental group received S-PBL and improved their critical thinking disposition and self-efficacy compared to the traditional learning method. In ordinary least square, affecting factors on critical thinking were the learning method and self-efficacy and these variables explained 41.0% in the critical thinking disposition. The results of the quantile regression method showed that affecting factors of critical thinking disposition were learning attitude of 0.1 quantile to 0.7 quantile and self-efficacy of all quantiles, and learning attitude of 0.4, 0.6, and 0.7 quantiles. Conclusion: The S-PBL is an effective method for nursing students who have low critical thinking disposition score to increase critical thinking disposition. And instructors can actively use S-PBL to enhance critical thinking disposition as well as self-efficacy in class.