• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.707-716
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• 2011

Quantiles and quantile ranks(or plotting positions) are widely used in academia and industry. Sample quantile methods and sample quantile methods implemented in some major statistical software are at least seven, respectively. Small looking differences between the methods can make big differences in outcomes that result from decisions based on them. We discussed the characteristics and differences of the basic plotting position using the empirical cumulative probability and the six plotting positions derived from the suggestion of Blom (1958). After discussing the characteristics and differences of seven quantile methods used in the some major statistical software, we suggested a general expression covering all seven quantile methods. Using the insight obtained from the general expression, we proposed four propositions that make it possible to find the plotting position method that correspond to each of the seven quantile methods. These correspondences may help us to understand and apply quantile methodology.

• 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 Korea Water Resources Association
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• v.38 no.1
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• pp.1-9
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• 2005

The study shows the possible use of the index flood frequency curves for an estimation of flood quantiles at ungauged locations. Flood frequency analysis were made for the annual maximum flood data series at 9 available stations in the Han river basin. From the flood frquency curve at each station the mean annual flood of 2.33-year return period was determined and the ratios of the flood magnitude of various return period to the mean annual flood at each station were averaged throughout the Han river basin, resulting mean flood ratios of different return periods. A correlation analysis was made between the mean annual flood and physiographic parameters of the watersheds i.e, the watershed area and mean river channel slope, resulting an empirical multiple linear regression equation over the whole Han river basin. For unguaged watershed the flood of a specified return period could be estimated by multiplying the mead flood ratio corresponding the return period with the mean annual flood computed by the empirical formula developed in terms of the watershed area and river channel slope. To verify the applicability of the methodology developed in the present study the floods of various return periods determined for the watershed in the river channel improvement plan formulation by the Ministry of Construction and Transportation(MOCT) were compared with those estimated by the present method. The result proved a resonable agreement up to the watershed area of approximately 2,000k $m^2$. It is suggested that the practice of design flood estimation based on the rainfall-runoff analysis might have to be reevaluated because it involves too much uncertainties in the hydrologic data and rainfall-runoff model calibration.

• The Korean Journal of Financial Management
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• v.24 no.3
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• pp.133-152
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• 2007

In this paper, we examine a portfolio selection model in which a safety-first investor maximizes expected return subject to a downside risk constraint. We use the Value-at-Risk as the downside risk measure. We exploit the fact that returns are fat-tailed, and use a semi-parametric method suggested by Jansen, Koedijk and de Vries(2000). We find a more realistic asset allocation than the one suggested by the literature based on the traditional mean-variance framework. For the robustness check, we provide empirical analyses using empirical quantiles. The results highlight that for optimal portfolio selection involving downside risks that are far in the tails of the distribution, our mean-VaR model with a fat-tailed distribution is superior.

• Journal of Korea Water Resources Association
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• v.46 no.6
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• pp.643-653
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• 2013

The most important procedure in frequency analysis is to determine the appropriate probability distribution and to estimate quantiles for a given return period. To perform the frequency analysis, the goodness-of-fit tests should be carried out for judging fitness between obtained data from empirical probability distribution and assumed probability distribution. The previous goodness-of-fit could not consider enough extreme events from the recent climate change. In this study, the critical values of the modified Anderson-Darling test statistics were derived for 3-parameter Weibull distribution and power test was performed to evaluate the performance of the suggested test. Finally, this method was applied to 50 sites in South Korea. The result shows that the power of modified Anderson-Darling test has better than other existing goodness-of-fit tests. Thus, modified Anderson-Darling test will be able to act as a reference of goodness-of-fit test for 3-parameter Weibull model.

• Journal of Korea Water Resources Association
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• v.40 no.10
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• pp.811-821
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• 2007

The intensity-duration-frequency (IDF) curves by Talbot, Sherman and Japanese type formulas are widely used in South Korea since the parameters are easily estimated. However, these IDF curves' accuracies are relatively worse than those of the IDF curves developed by Lee et al. (1993) and Heo et al. (1999), and different parameters for the given return periods should be computed. In this study, parameter estimation method for the IDF curve by Heo et al. (1999) is suggested using genetic algorithm (GA). Quantiles computed by at-site frequency analysis using the rainfall data of 22 rainfall gauges operated by Korea Meteorological Administration are employed to estimate the parameters of IDF curves and minimizing root mean squared error (RMSE) and relative RMSE (RRMSE) of observed and computed quantiles are used as objective functions of GA. The comparison of parameter estimation methods between the empirical regression analysis and the suggested method show that the IDF curve in which the parameters are estimated by GA using RRMSE as an objective function is superior to the IDF curves using RMSE.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.233-260
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• 2022

In this paper, we analyzed how oil price fluctuations affect stock price by industry using the non-parametric quantile causality test method. We used weekly data of WTI spot price, KOSPI index, and 22 industrial stock indices from January 1998 to April 2021. The empirical results show that the effect of changes in oil prices on the KOSPI index was not significant, which can be attributed to mixed responses of diverse stock prices in several industries included in the KOSPI index. Looking at the stock price response to oil price by industry, the 9 of 18 industries, including Cloth, Paper, and Medicine show a causality with oil prices, while 9 industries, including Food, Chemical, and Non-metal do not show a causal relationship. Four industries including Medicine and Communication (0.45~0.85), Cloth (0.15~0.45), and Construction (0.5~0.6) show causality with oil prices more than three quantiles consecutively. However, the quantiles in which causality appeared were different for each industry. From the result, we find that the effects of oil price on the stock prices differ significantly by industry, and even in one industry, and the response to oil price changes is different depending on the market situation. This suggests that the government's macroeconomic policies, such as industrial and employment policies, should be performed in consideration of the differences in the effects of oil price fluctuations by industry and market conditions. It also shows that investors have to rebalance their portfolio by industry when oil prices fluctuate.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.165-174
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• 2019

As a nonstationarity is observed in hydrological data, various studies on nonstationary frequency analysis for hydraulic structure design have been actively conducted. Although the inherent diversity in the atmosphere-ocean system is known to be related to the nonstationary phenomena, a nonstationary frequency analysis is generally performed based on the linear trend. In this study, a nonstationary frequency analysis was performed using climate indices as covariates to consider the climate variability and the long-term trend of the extreme rainfall. For 11 weather stations where the trend was detected, the long-term trend within the annual maximum rainfall data was extracted using the ensemble empirical mode decomposition. Then the correlation between the extracted data and various climate indices was analyzed. As a result, autumn-averaged AMM, autumn-averaged AMO, and summer-averaged NINO4 in the previous year significantly influenced the long-term trend of the annual maximum rainfall data at almost all stations. The selected seasonal climate indices were applied to the generalized extreme value (GEV) model and the best model was selected using the AIC. Using the model diagnosis for the selected model and the nonstationary GEV model with the linear trend, we identified that the selected model could compensate the underestimation of the rainfall quantiles.