• Environmental and Resource Economics Review
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• v.29 no.2
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• pp.171-194
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• 2020

We revisit the impact of oil shocks on the Korean economy and examine how this impact varies depending on a business cycle. First, we estimate the probability of a recession through a logistic probability distribution, and correct the probability to match business cycles announced by the Korea National Statistical Office. We set up a STVAR model to analyze the response of macroeconomic variables to oil shocks according to business cycles. We find that oil shocks during the recession have a negative effect on GDP in the mid- and long-term, but during the expansion, GDP does not show a statistically significant response to oil shocks. We presume that this finding is associated with the factors of both the increase in demand for consumption and the increase in current account during the economic boom. Also, we find that the impact of oil shocks on the price level was also observed differently in terms of the persistence of inflation by business cycle. These results highlight the importance of an application of a regime switching model, which has been widely used in energy economics in recent years.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.2
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• pp.217-242
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• 2006

This Study reviewed 86 research articles using inferential statistics published in 2001-2004 in 4 korean core journals in the field of library and information science. Sampling methods, response rates and nonresponse bias, reliability test, and inferential statistic techniques used in the articles were critically reviewed and analyzed. Nonprobability sampling was mostly used. Average response rate was 74.47%. Parametric statistics were mostly used. Some misunderstandings in using each inferential statistics, especially Reliability Test, Multiple Regression, Factor Analysis, MDS, etc. were reported in this study.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.29-41
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• 2017

Design rainfalls are one of the most important hydrologic data for river management, hydraulic structure design and risk analysis. The design rainfalls are first estimated by a point frequency analysis and the IDF (intensity-duration-frequency) curve is then constructed by a nonlinear regression to either interpolate or extrapolate the design rainfalls for other durations which are not used in the frequency analysis. It has been widely recognised that the more reliable approaches are required to better account for uncertainties associated with the model parameters under circumstances where limited hydrologic data are available for the watershed of interest. For these reasons, this study developed a hierarchical Bayesian based GLM (generalized linear model) for a regional frequency analysis in conjunction with a scaling function of the parameters in probability distribution. The proposed model provided a reliable estimation of a set of parameters for each individual station, as well as offered a regional estimate of the parameters, which allow us to have a regional IDF curve. Overall, we expected the proposed model can be used for different aspects of water resources planning at various stages and in addition for the ungaged basin.

• Spatial Information Research
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• v.8 no.1
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• pp.85-99
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• 2000

Accurate information on habitat distribution of protected fauna is essential for the habitat management of Korea, a country with very high development pressure. The objectives of this study were to develop a habitat suitability model of wild boar based on GIS and logistic regression, and to create habitat distribution map, and to prepare the basis for habitat management of our country s endangered and protected species. The modeling process of this restudyarch had following three steps. First, GIS database of environmental factors related to use and availability of wild boar habitat were built. Wild boar locations were collected by Radio-Telemetry and GPS. Second, environmental factors affecting the habitat use and availability of wild boars were identified through chi-square test. Third, habitat suitability model based on logistic regression were developed, and the validity of the model was tested. Finally , habitat assessment map was created by utilizing a rule-based approach. The results of the study were as folos. First , distinct difference in wild boar habitat use by season and habitat types were found, however, no difference in wild boar habiat use by season and habitat types were found , however, ho difference by sex and activity types were found. Second, it was found, through habitat availability analysis, that elevation , aspect , forest type, and forest age were significant natural environmental factors affecting wild boar hatibate selection, but the effects of slope, ridge/valley, water, and solar radiation could not be identified, Finally, the habitat at cutoff value of 0.5. The model validation showed that inside validation site had the classification accuracy of 73.07% for total habitat and 80.00% for cover habitat , and outside validation site had the classification accuracy of 75.00% for total habitat.

• KDI Journal of Economic Policy
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• v.36 no.3
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• pp.1-23
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• 2014

This paper has studied the monetary policy in Korea with a time varying VAR model using four key macroeconomic variables. First, inclusion of the exchange rate was a crucial factor in evaluating Korean monetary policy since the monetary policy demonstrated sensitivity to exchange rate movements during the crisis periods of both the Asian financial crisis of 1997 and the global financial crisis of 2008. Second, a specification of the stochastic volatilities in TVP-VAR model is important in explaining excessive movements of all variables in the sample. The overall moderation of variables in 2000s was more or less due to a reduction of the stochastic volatilities but also somewhat due to the macroeconomic fundamental structures captured by impulse response functons. Third, the degree of the monetary policy effectiveness of inflation was mitigated in recent periods but with increased persistence. Lastly, the monetary policy stance towards inflation stabilization has advanced ever since the inflation targeting scheme was adopted. However, there still seems to be a room for improvement in this aspect since the degree of the monetary policy stance towards inflation stabilization was relatively weaker than to output stabilization.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.332-332
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• 2021

도시화가 상당히 이뤄지고 기습적인 폭우의 발생이 불확실하게 나타나는 시점에서 재산 및 인명피해를 야기할 수 있는 내수침수에 대한 위험도가 증가하고 있다. 내수침수에 대한 예측을 위하여 실측강우 또는 확률강우량 시나리오를 참조하고 연구대상 지역에 대한 1차원 그리고 2차원 수리학적 해석을 실시하는 연구가 오랫동안 진행되어 왔으나, 수치해석 모형의 경우 다양한 수문-지형학적 자료 및 계측 자료를 요구하고 집약적인 계산과정을 통한 단기간 예측에 어려움이 있음이 언급되어 왔다. 본 연구에서는 위와 같은 문제점을 해결하기 위하여 단일 도시 배수분구를 대상으로 관측 강우 자료, 1, 2차원 수치해석 모형, 기계학습 및 딥러닝 기법을 적용한 실시간 홍수위험지도 예측 모형을 개발하였다. 강우자료에 대하여 실시간으로 홍수량을 예측할 수 있도록 LSTM(Long-Short Term Memory) 기법을 적용하였으며, 전국단위 강우에 대한 다양한 1차원 도시유출해석 결과를 학습시킴으로써 예측을 수행하였다. 침수심의 공간적 분포의 경우 로지스틱 회귀를 이용하여, 기준 침수심에 대한 예측을 각각 수행하였다. 홍수위험 등급의 경우 침수심, 유속 그리고 잔해인자를 고려한 홍수위험등급 공식을 적용하여 산정하였으며, 이 결과를 랜덤포레스트(Random Forest)에 학습함으로써 실시간 예측을 수행할 수 있도록 개발하였다. 침수범위 및 홍수위험등급에 대한 예측은 격자 단위로 이뤄졌으며, 검증 자료의 부족으로 침수 흔적도를 통하여 검증된 2차원 침수해석 결과와 비교함으로써 예측력을 평가하였다. 본 기법은 특정 관측강우 또는 예측강우 자료가 입력되었을 때에, 도시 유역 단위로 접근이 불가하여 통제해야 할 구간을 실시간으로 예측하여 관리할 수 있을 것으로 판단된다.

• Journal of the Korean Society for Railway
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• v.18 no.4
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• pp.374-390
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• 2015

This research shows the relationship between the number of trains and the probability of trains with arrival delay and suggests way to estimate the benefits of improved punctuality in a bottleneck section of the Gyeongbu Line. The arrival delays of high-speed and conventional trains were estimated using the train operation data of KORAIL. Linear regression models for the probability of trains with arrival delay by train type are presented in this paper. The probabilities of trains with arrival delay were more affected by the number of conventional trains than by the number of high-speed rail trains. For the empirical analysis, a project for increasing the capacity in the Seoul~Geumcheongu office section was tested. The benefits of the improved punctuality were estimated to be 4.2~4.5 billion Korean won every year. This research has some limitations but it can help evaluate more precisely the feasibility of the project of increasing the capacity in bottleneck sections.

• Journal of Technology Innovation
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• v.20 no.3
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• pp.57-91
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• 2012

This paper investigates determinants of litigation success in the two distinctive types of patent litigations, ex-parte and inter-parte cases, which are brought in the process where a filed application becomes a valid patent right. We regress winning rates of patent applicants on the characteristics of firms, trials, patent lawyer, and patent itself, using a probit model with sample selections. The paper finds that the relative suit rate of a firm, time to be sued, changes of patent agents by applicants, and multiple agents among explanatory variables affect ex-parte reexamination and in-parte post-grant patent trials differently in the point of average marginal effects. These variables lower the probability of applicant's victory in the ex-parte cases, while they raise the probability in the inter-parte trials. However, the experience that agents represent applicants is a winning rate-increasing factor both in inter-parte and ex-parte reexamination, unexpectedly. This result cannot be applied to the entire domain of the variable, since sample selection effects are reflected in the result. The number of claim increases the winning probability of the applicant in the both types of patent litigations. This study has some limitations because it ignores the information on the legal person to which a patent agent belongs, and confined agent's experience to patent filing. We leave it future studies to investigate the effects of lawsuit experience of patent agent, and those of characteristics of the law firm to which individual patent lawyer is affiliated.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.212-222
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• 2020

The purpose of this study is to understand the current status and trends of the research studies published by the Society for Industrial and Applied Mathematics which is a leader in the field of industrial mathematics around the world. To perform this purpose, titles and abstracts were collected from 6,255 research articles between 2016 and 2019, and the R program was used to analyze the topic modeling model with LDA techniques and a regression model. As the results of analyses, first, a variety of studies have been studied in the fields of industrial mathematics, such as algebra, discrete mathematics, geometry, topological mathematics, probability and statistics. Second, it was found that the ascending research subjects were fluid mechanics, graph theory, and stochastic differential equations, and the descending research subjects were computational theory and classical geometry. The results of the study, based on the understanding of the overall flows and changes of the intellectual structure in the fields of industrial mathematics, are expected to provide researchers in the field with implications of the future direction of research and how to build an industrial mathematics curriculum that reflects the zeitgeist in the field of education.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.413-413
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• 2022

본 연구에서는 통계적 방법으로 도출된 장기 기상예측정보를 이용하여 유역에서의 유출량 전망 가능성을 검토하였다. 먼저 한강권역의 월 강수량과 기온에 대해 글로벌 기후지수와의 원격상관성을 기반으로 다중회귀모형 기반의 통계적 예측모형을 구성하여 미래기간(1~12개월)에 대한 월 단위 기상예측정보를 도출하였다. 월 단위로 도출된 강수량과 기온은 통계적 상세화 기법을 통해 한강권역 주요 ASOS 관측소 지점별로 일 단위 강수량과 기온자료로 변환하였으며, 상세화된 일 자료를 유역모형인 SWAT의 입력자료로 활용하여 경안천 유역의 미래기간에 대한 유출량을 도출하였다. 유출량 예측성을 평가하기 위하여 과거기간(2003~2021년)을 대상으로 관측유출량과 예측기상정보로부터 산출된 예측유출량을 비교하였다. 각 월별로 예측된 유출량의 중앙값과 관측값의 적합도를 분석한 결과, PBIAS는 -5.2~-2.7%, RSR은 0.79~0.91, NSE는 0.34~0.38, r은 0.59~0.62로 강수량 및 기온의 예측성에 비해 낮게 나타났다. 전 기간에 대해 월별로 분석한 예측결과에 대한 3분위 확률은 5월, 6월, 7월, 9월, 11월은 평균 42.8%로 예측성이 충분한 것으로 나타났으나, 나머지 월에서의 평균 예측성은 17.3%로 매우 낮게 나타났다. 상세화된 기상정보를 이용하여 유역모델링을 통해 산정한 유출량에 대한 전망 결과는 기상예측결과에 비해 상대적으로 예측성이 낮은 것으로 분석되었다. 이는 관측값 자체에서 나타날 수 있는 불확실성에 기인할 수도 있으며, 유출량에 지배적인 영향을 주는 강수량의 예측성에 대한 문제가 유역 모델링 과정에서 증폭되어 나타나는 문제일 수도 있다. 또한 지점별 일 자료로 상세화되는 과정에서의 불확실성, 우리나라 여름철 유출량 변동성 등 여러 가지 요인이 복합적으로 영향을 주어 나타나는 것으로 생각된다. 향후 다양한 대상유역에 대한 검토와 기상예측모형의 보완, 상세화 과정에서의 불확실성 해소 등을 통해 예측성을 개선할 계획이다.