Earticle

본 연구는 연구개발특구 내 첨단기술기업으로 지정된 제조업 기업에 대하여 경영성과를 바탕 으로 경제적 효과성 분석을 하였다. 한국기업데이터(CRETOP)로부터 확보한 전국 연구개발특구 내 첨단기술기업으로 지정된 기업들의 재무제표와 한국신용평가(NICE Co.)에서 획득한 전국 제 조업 업종에 해당하는 기업 중 외부감사 대상 기업의 재무제표(KIS-Data) 등을 활용하여 불균 형 패널자료를 구축하였다. 특구내 첨단기술기업 지정기업의 성과가 유의미하게 좋은지를 확인 하기 위하여 임의효과 패널분석(random effect panel regression)과 함께 성향점수매칭 (propensity score matching)을 활용하여 기업의 성장성, 고용 및 생산성 측면에서 효과가 있는 지 살펴보았다. 분석 결과, 첨단기술기업 지정으로 받는 법인세 등 감면의 세제지원 효과로 인 하여 기업의 성장성, 고용, 생산성 등이 유의미하게 나타나며 성과 측면에서 첨단기술기업 지정 으로 이러한 세액감면 효과를 받지 않은 기업의 경영성과와 차별화된 점을 확인하였다. 이러한 결과는 연구개발특구 내 첨단기술기업 지정제도가 첨단기업의 성장과 육성에 효과성이 있다는 점을 시사한다는 점에서 연구개발특구 제도가 본래 목적에 부합하게 효과를 창출하고 있다고 할 수 있을 것이다.

This paper evaluates the economic effectiveness of manufacturing firms which are designated as high-technology firms in the R&D specialized zone by their financial performance. We construct panel data by using the financial statements of high-tech firms obtained from CRETOP and the financial statements of firms that subject to external audit in the manufacturing industry in Korea from NICE Co. We examines whether the designation of high-tech firms have significant effects on their growth, employments, and productivities by using propensity score matching (PSM) and random effect panel analysis. As a result of the analysis, the tax credit effect of firms that are designated as high-tech firms show significant performances in terms of growth, employments, and productivities. This suggests that this programme is an example of contributing to industrial growth by proving that it is effective in the growth of R&D and fostering high-tech industries.

Porous filters are widely used to control air pollution and have different industrial applications since they constitute a reliable and low cost solution to separate particulate matter from an air stream. In this study, the particle deposition within 3D porous filters subjected to low-frequency acoustic fields is studied following a numerical approach. Findings demonstrate that the application of acoustic waves enhances the deposition of particles, which in turn improves filter performance. It is shown that frequencies ranging from 200 to 1000 Hz (intensity 120 dB) increase particle deposition up to 2.5 times. Besides, the manner in which fibres are distributed in the porous material and the filter porosity affect considerably the number of particles deposited, for filters subjected to the same filtration velocity.

토석류는 우리나라 주요 산지토사재해 중 하나로, 토석류로 운송된 다량의 토사가 하류 지역에 퇴적 되어 인명 및 재산피해를 일으키기 때문에 토석류로 인한 피해 범위를 예측하여 경계 피난을 위한 기초자료로 활용하는 것이 중요하다. 우리나라 산림청은 Random Walk Model(RWM)을 활용하여 토석류 피해예측지도를 구축하였다. 한편, 산지계류에 설치되는 사방댐은 토석류 유출을 차단하여 피해면적을 감소시키므로 효과적인 토석류 피해예측지도 활용을 위해서는 사방댐의 효과가 고려되 어야 한다. 이 연구에서는 강원도 소재 9개 사방댐 설치 유역에 대해, 사방댐의 효과를 고려한 RWM 토석류 유출 및 퇴적 모의를 통해 토석류 피해예측지도에 대한 사방댐 효과 적용 가능성을 검토하였 다. 분석결과 RWM을 통해 사방댐의 효과는 일정 부분 재현 가능하였으나, 입력 매개변수 및 모델 구성의 한계로 피해예측범위의 수정은 제한될 것으로 판단되었다. 향후 공간해상도의 개선 및 합리 적 붕괴토사량의 산정을 통해 사방댐의 효과를 반영한 효과적인 토석류 피해예측지도 구축이 가능 할 것으로 판단된다.

Debris flow transports lots of sediments downstream that cause damage to human lives and properties. The accurate estimation of the debris flow hazard zone is a critical factor of the successful early-warning and evacuation for reducing the debris flow risk. Korea Forest Service has developed a debris flow hazard map using Random Walk Model (RWM). Meanwhile, because check dams in forest watersheds capture discharged debris, the effect of check dams should be considered for mapping the debris flow hazard zone. This study analyzed the effect of check dams on debris flow discharge using RWM with the check dam module. As a result, RWM seems to simulate the sediment capture by check dams. However, the total deposit areas were not significantly different despite the effect of check dams because of input parameters and the flow direction calculation algorithm of RWM. Further studies on appropriate spatial resolution and initial sediment volume should be conducted to improve the debris flow hazard map.

본 연구는 프로스포츠 관람객이 지각하는 팀동일시가 팀충성도, 팀이미지 그리고 그들의 만족에 미치는 영향에 대한 효과분석을 도출하는 데 목적이 있다. 이를 위해 한국연구재단 등재 KCI학술지(2021년 12월까지)에서 발표된 연구논문과 미간행 학위논문 64편의 선행연구들을 선정하였다. 최종 선별과정 4단계를 거쳐 15편의 논문 중 총 표본은 7,201개, 효과 크기는 34개를 분석대상으로 선별하였다. 선별된 자료의 구체적인 자료처리는 CMA V.2 메타분석 소프트 웨어 프로그램을 사용하여 진행되었다. 이를 통해 출판편향 여부와 변인 간 영향에 대한 상관 효과 크기를 분석하여 다음과 같은 결과를 얻었다. 첫째, Funnel plot 확인 결과 출판편향이 존재하지 않았으며, 개별적 효과 크기의 전체적 분포를 알아보기 위해 깔때기 도표를 제시하였다. 둘째, 팀동일시 요인의 이질성 검사에서는 높은 이질성이 존재하여 랜덤 효과 모형을 이용해 전체적인 효과 크기를 측정하였다. 셋째, 각 변인에 대한 전체 효과 크기를 산출한 결과 팀충성도의 효과 크기(95% CIs [.612, .718] ES=.668)는 전반적으로 중간 수준이었으며 팀이미지와 만족에 대한 효과 크기는 중간 수준보다는 밑돌고 있는 것으로 분석되었다.

This study is to derive an effect analysis on the effect of team identification perceived by professional sports visitors on team loyalty, team image, and their satisfaction. For this, research papers, published in KCI journals(until August 2020) registered by the National Research Foundation of Korea and 64 unpublished dissertations were selected. After a four-stage screening process, 7,201 total samples and 34 effect sizes were selected for analysis in 15 papers. Specific data processing of selected data used Meta Analysis Version 2(CMA) software. Through this, the following results were obtained by analyzing the size of the correlation effect on the publication bias and the influence between variables. First, Funnel plot confirmation showed no publication bias, and a Funnel plot was presented to examine the overall distribution of individual effect sizes. Second, in the heterogeneity test of team identification factor, high heterogeneity was present, so the overall effect size was measured using a random effect model. Third, as a result of calculating the overall effect size for easch variable, the overall effect size of team loyalty(95% CI [.612, .718] ES=.668) was in the middle level, and the effect size on team image and satisfaction was lower that that of the middle level.

검지기 자료의 품질은 동일한 교통 조건이라도 검지기의 종류, 보정 및 유지 보수의 노력, 도로의 기하구조 등에 의해 내재적으로 10% 이상 변수 값 차이를 보일 수 있다. 이러한 내재적인 변수 값 차이로 인해 성능이 검증된 돌발상황 자동감지알고리즘을 사용하더라도 검지율(DR)이 낮아지고 오보율(FAR)이 높아지는 문제가 야기될 수 있다. 본 연구는 교통특성 변수의 간단한 산술계산기법(SAO)를 활용하여 새로운 AIDA를 개발하는데 목적이 있다. SAO는 내재적으로 존재하는 변수 값 차이를 보정하여 AIDA의 성능 저하를 극복하기 위해 개발되었다. AIDA 모형의 개발 및 검증을 위해 내부순환도로와 경부고속도로에서 수집된 30초 단위의 점유율을 사용하였다. SAO 기반 알고리즘 분석 결과 100% 돌발상황을 검지하는 높은 검지율을 보였고, 검지시간(MTTD)은 1분 이내로 나타났다. 오보율의 경우 본 연구에서 개발된 SAO 기반 AIDA의 오보율이 기개발된 AIDA의 오보율보다 3.5%~31.0% 감소하는 것을 확인하였다. SAO 기반의 알고리즘은 돌발상황의 지속성 분석에서도 탁월한 능력을 보였다.

The data quality and measurements along consecutive detector stations can vary much even in the same traffic conditions due to variety in detector types, calibration and maintenance effort, field operation periods, minor geometric changes of roads and so on. These faulty situations often create 10% or more of inherent difference in important traffic measurements between two stations even under stable low flow condition. Low detection rates(DR) and high false alarm rates(FAR) therefore sets in among many popular Automatic Incident Detection Algorithms(AIDA). This research is two-folded and aims mainly to develop a new AIDA for uninterrupted flow. For this purpose, a technique which utilizes a Simple Arithmetic Operation(SAO) of traffic variables is introduced. This SAO technique is designed to address the inherent discrepancy of detector data observed successive stations, and to overcome the degradation of AIDA performance. It was found that this new algorithm improves DR as much as 95 percent and above. And mean time to detection(MTTD) is found to be 1 minutes or less. When it comes to FAR, this new approach compared to existing AIDAs reduces FAR up to 31.0 percent. And capability in persistency check of on-going incidents was found excellent as well.

심해나 해상조건이 나쁜 지점에서 운영될 해양구조물의 설계시에는 파랑하중에 대한 구조물의 피로수명예측이 매우 중요하다. 본 논문에서는 랜덤파랑하중에 의해 해양구조물에 발생되는 응력의 bandwidth가 구조물의 피로수명예측 결과에 주는 영향에 대하여 연구하였다. 구조물의 동적거동해석은 비선형 점성저항력의 선형화를 통한 주파수영역해법을 사용하여 수행되었으며, 랜덤응력스펙트럼을 구하고 이를 바탕으로 피로해석을 수행하였다. 피로손상의 예측을 위한 랜덤응력 cycle의 산정은 narrow band cycle 산정법과 함께 wide band process 에 대하여 더 적정한 rainflow cycle 산정법을 사용하였다. 수심이 다른 지점에 위치한 2개의 구조물을 택하여 예제해석을 수행하였으며, 두 응력 cycle 산정법에 의해 얻은 피로해석 결과들을 비교하고 응력의 bandwidth의 영향을 분석하였다.

Fatigue life is an important consideration for the design of offshore structures in deep sea and in hostile environments. In this paper, the effect of the bandwidth of random stresses on the fatigue life estimation of fixed offshore structures is investigated. The dynamic analysis is carried out in the frequency domain by using the equivalent linearization technique. Fatigue damages are calculated by two stress cycle counting methods; i.e., the narrow band method and the wide band method using rainflow counting technique. Example studies are carried out for two different structures. Numerical results indicate that the wide band approach, which is more complex but theoretically more appropriate pridicts smaller values of fatigue damages compared with those by the narrow band approach for all seastate conditions. Such trend becomes more apparent for the cases of severe seastates where the bandwidth of random stresses becomes large.

본 연구는 임의회귀검정일 모형을 이용한 유전능력평가 수행시 낭우들의 불완전비유기록이 생산형질의 육종가에 미치는 영향에 대하여 연구하기 위하여, 완전기록(complete lactation records)을 가지고 있는 낭우들의 아비소에 대하여 임의회귀 검정일 모형을 이용하여 유전 모수와 육종가를 추정하고, 이중 특정 아비소의 낭우들의 완전기록을 다양한 방법으로 불완전기록화하여 다시 육종가를 추정함으로써 이들 결과를 비교하고, 불완전 기록을 가지고 있는 낭우들에 의하여 평가된 후보우들에 대한 육종가 평가의 오류를 최소화 시키는 방법을 모색하고자 수행하였다. 분산성분추정은 REMLF90(Misztal, 2001)을 이용하였으며, 육종가의 추정은 BLUPF90(Misztal, 2001)을 이용하였다. 추출된 아비소의 낭우들을 선택하여 각각 검정일 90일, 150일, 200일 이후 자료를 삭제한 후 각각의 검정일 자료를 이용하여 육종가를 추정하여 비교 하였다. 유량에 대한 완전기록(rank_full)과 불완전기록(rank_90cut, 150cut, 200cut)과의 육종가에 의한 순위는 매우 상이하게 나타났으며, 특히, 150일, 200일 이하의 낭우 기록만을 이용한 아비소의 육종가 순위 변동보다 90일 이하의 낭우 기록만 이용한 아비소의 육종가 순위 변동이 상대적으로 매우 크게 나타났다. 비유지속성(persistancy)에 대한 육종가는 완전기록(per305_full)과 불완전기록(per_90cut, 150cut, 200cut)과의 차이가 나타났으며, 특히 90일 이하의 낭우기록만으로 추정된 비유지속성에 대한 육종가(per_90cut)는 완전기록을 이용한 아비소의 유량에 대한 비유 지속성의 차이가 적게는 1.8kg에서 많게는 145kg 까지 매우 크게 나타났다. 본 연구의 결과는, 우리나라에서 수행되고 있는 후대 검정사업에 검정일 임의 회귀모형을 적용할 경우, 305일 보정방법에 의한 유전평가보다 많은 장점을 취할 수 있으나, 어린 후보우(young bull)들의 낭우들이 불완전기록만을 가지고 있는 상태에서 평가되어 질 경우, 우수한 후보종모우가 선발대상에서 탈락될 수 있는 위험이 있으므로 정확한 종모우의 선발을 위해서는 가급적 낭우의 완전기록을 포함된 기록을 이용하여야 한다는 것이다.

The purpose of this study was to find out the effects that daughters’ incomplete lactation records affect sire’s breeding values through genetic evaluation using RRTDM(random regression test-day model). First, we estimated genetic parameters and breeding values on sires having complete lactation records of daughter by RRTDM, second, we changed complete lactation records of specific sires into incomplete records by various methods. Third, the breeding values were compared between complete and incomplete records. Finally, this study aimed to find out the methods to minimize the estimation errors of young bulls’ breeding values. Data used in this study were collected from the dairy herd improvement program, and a total of 97,562 records were composed of 10,929 first parity with both parents known, since 1999. Breeding values on the daughters from randomly chosen sires were calculated and compared with among 90day, 150day, and 200day’s incomplete records. For milk yields, sire’s ranks of breeding values used by complete lactation records were very different from sire’s ranks of breeding values obtained by incomplete lactation records(Rank_90cut, 150cut, 200cut).The differences were also obtained between complete lactation records(per305_full) and incomplete lactation record (per_90cut, 150cut, 200cut) in breeding values regarding persistency. Especially, the differences between per_90cut and per305_full were very large(from 1.8kg to 145kg).

본 연구는 소비자의 인지적/정서적 의사결정 방식이 랜덤박스의 구매의도에 미치는 영향을 검증하고자 하였다. 구체적으로 본 연구에서는 개인의 인지적/정서적 의사결정 성향과 광고메시지로 유도한 인지적/정서적 의사결정 방식이 랜덤박스 구매의도에 미치는 영향을 확인하고자 하였다. 연구 1 에서는 개인이 지닌 의사결정 성향이 랜덤박스 구매의도에 미치는 영향을 확인하였다. 81명을 대상으로 한 연구 결과, 정서적 의사결정 성향이 높을수록 랜덤박스의 불확실성을 긍정적으로 평가하여 더 높은 랜덤박스 구매의도를 보일 것이라는 가설이 지지되었다. 반면, 개인의 인지적 의사결정 성향이 높을수록 랜덤박스의 위험성을 높게 인식하여 낮은 랜덤박스 구매의도를 보였다. 연구2에서는 광고메시지를 활용한 점화된 의사결정 방식이 랜덤박스 구매의도에 미치는 영향을 확인하고 의사결정 방식과 위험성 평가가 랜덤박스 구매의도에 미치는 영향에 대한 랜덤박스 할인 단서의 조절된 매개 효과를 검증하였다. 연구 결과, 광고메시지 유형이 랜덤박스 구매의도에 미치는 영향에 대한 긍정적 불확실성의 매개효과와 위험성 평가와 할인단서의 조건부 매개효과가 나타났다. 구체적으로 할인단서가 있는 조건에서는 의사결정 방식에 따른 위험성평가와 구매의도의 차이가 줄어들었다. 연구 결과를 바탕으로 한계점 및 추후연구방향과 연구의 시사점을 논의하였다.

The purpose of this study was to verify the effect of the decision mode on the consumer's willingness to buy for Random Box. Specifically, it was intended to identify the effect of individuals' cognitive/affective decision-making styles and cognitive/affective decision mode induced by advertising messages on their willingness to purchase random box. Two studies were conducted and study 1 identified the mediating role of positive uncertainty and risk assessment with the decision-making styles on the willingness to purchase random box. Study 2 examined the effect of decision mode induced through advertising message and discount cue on positive uncertainty, risk assessment and willingness to purchase random box. Result indicated that willingness to purchase random box was mediated by positive uncertainty. Conditional mediation effect of discount cue and risk assesment were identified. It was found that in the discount cue condition, the difference between advertising message condition on the risk assessment and the willingness to purchase random box was reduced. The implications, limitations, and future research directions were discussed.

As the climate problem becomes more serious, mitigating the heat island effect is becoming an important issue. To solve the problem, it is necessary to identify the areas where the heat island effect is more severe and present appropriate measures to those areas. In this paper, we divided Seoul into 573 grids and divided into four clusters by hierarchical clustering techniques to identify the current status of the city's heat island effect. Of these cluster, we chose the one with the most urban features. We extracted the variable importance with random forest models and developed the heat island index from 1) multiplication of the variable importance and 2) the sign of the correlation coefficient between temperature and 3) the corresponding variable. Furthermore we analyzed areas where the heat island effect is severe with the index. According to the analysis, the heat island effect was particularly severe in five grids, and we suggested ways to mitigate the heat island effect for the areas concerned.

The purpose of this paper is to test the productivity bias hypothesis that real exchange rate could deviate from its long-run equilibrium. To analyze the hypothesis, we estimated the long-run relationship between real exchange and productivity using the random effect, fixed effect and dynamic panel data model for 89 countries. The empirical results suggest that the increase in relative labor productivity difference, defined as trade sector productivity over non-trade sector productivity, causes real exchange rate to appreciate within 1% significance level no matter what of estimating model. Then we implemented the robustness analysis of the result by national income and exchange regime. The result shows that in most cases, the positive relative productivity differential has to do with the positive change of real exchange which means that the productivity bias hypothesis is supported.

In this study, we test some hypotheses on whether foreign-invested companies in Busan have created jobs and spill-over effect on Busan local economy through backward and forward linkage with domestic firms. Panel regressions on both firm-level and macro data show that FDIs have directly increased jobs in the investing companies and also have indirectly increased sales in upstream and downstream companies. When the same regressions are run for two sub-samples, manufacturing vs. service industries, backward linkage effect turns out greater than forward linkage one in both cases.

The purpose of this study is to investigate whether recent investment in the Korean energy sector has affected the increase in the share of renewable energy and to find this result has an effect on the decrease of carbon emission sequentially. We used panel model and OLS to grasp these relations. The data used in this study are from Foreign investment in Korea power sector for 12 years by the Export-Import Bank of Korea (2004 ~ 2016) as a time-series data, and a panel comprising 17 wide-area regions across the country as a cross-section. The result of this study shows that the foreign investment increased the production of new renewable energy and reduces carbon emissions.

The Korean economy has achieved the high rates of economic growth during the last four decades through the export-led growth strategy by the government. It is well recognized that the export in the Korean manufacturing industries has been still leading the Korean economic growth. The purpose of this paper is to investigate the importance of industry-specific variables in determining the export performance of manufacturing industries in Korea during the period 1975:1-2010:4 using data pooling analysis. Our main findings are summarized as follows. The effect of markup on export growth is highly significant with positive sign. The rapid declines in interest rates and import prices since the foreign crisis occurred should have resulted in the cost advantages in exporting industries, which in turn could have substantially contributed to export growth. Thus, it may be concluded that the ratio of capital to labor and the ratio of foreign intermediate goods to industrial output are significantly industry-specific determinants in explaining the export performance.

Clustered data may have not only a correlation within-cluster but also a heterogeneity between-cluster. Thus, various random-effect models including generalized linear mixed models(GLMMs) have been widely used. However, the analysis of heterogeneity has been less studied. For this the prediction of random effects is very useful. In this paper we show a general framework how to model the clustered binominal data, and to estimate parameters and to predict random effects via SAS PROC NLMIXED. Thus, we illustrate the analyses with a real data set obtained from different 8 clinics for investigating a treatment effect. We demonstrate using this data set how an appropriate model can be selected via well-known model-selection criteria. We also present a plot on predicted random effects for investigating practically a potential heterogeneity over clinics.

In this paper we consider semiparametric random effect panel models that contain AR(p) disturbances. We derive the efficient score function and the information bound for estimating the slope parameters. We make minimal assumptions on the distribution of the random errors, effects, and the regressors, and provide semiparametric efficient estimates of the slope parameters. The present paper extends the previous work of Park et al.(2003) where AR(1) errors were considered.

This paper proposes using hierarchical-likelihood estimation method for binary panel data models featuring state dependence and unmeasured heterogeneity. Hierarchical generalized linear models are an extension of generalized linear models in that they combine generalized linear models with random effects. Hierarchical likelihood based upon hierarchical generalized linear models provides a useful tool for analyzing multivariate data with correlation. These models allow various regression models for mean parameters of response variable as well as dispersion parameters. For a inference, the hierarchical- likelihood approach is suggested for a useful too as model selection and residual plots with real data analysis. In this paper, we suggest hierarchical-likelihood approach estimators for binary random effects-models, and show that this approach outperforms existing marginal likelihood estimators at low computation cost by simulation studies. And then, we add its usefulness by analyzing a real example.

Simple frailty models such as shared frailty have been often used for analyzing clustered time-to-event data. However, under more complex structures such as nested design or multi-center trial, the extended models are required. In this paper we review multi-component semi-parametric survival models with various random-effect structures for analyzing the complex clustered time-to-event data. For the inferences we use hierarchical likelihood (h-likelihood), and also review how to select a proper model and also to select variables of fixed effects, which are very important in regression analysis. We discuss the relative advantages of h-likelihood method compared to marginal likelihood method. The h-likelihood method is illustrated using a well-known practical data set.

In this paper, we develop the Bayesian model selection procedure using the reference prior for comparing two nested model such as the independent and intraclass models using the distance or divergence between the two as the basis of comparison. A suitable criterion for this is the power divergence measure as introduced by Cressie and Read(1984). Such a measure includes the Kullback -Liebler divergence measures and the Hellinger divergence measure as special cases. For this problem, the power divergence measure turns out to be a function solely of $\rho$, the intraclass correlation coefficient. Also, this function is convex, and the minimum is attained at $\rho=0$. We use reference prior for $\rho$. Due to the duality between hypothesis tests and set estimation, the hypothesis testing problem can also be solved by solving a corresponding set estimation problem. The present paper develops Bayesian method based on the Kullback-Liebler and Hellinger divergence measures, rejecting $H_0:\rho=0$ when the specified divergence measure exceeds some number d. This number d is so chosen that the resulting credible interval for the divergence measure has specified coverage probability $1-{\alpha}$. The length of such an interval is compared with the equal two-tailed credible interval and the HPD credible interval for $\rho$ with the same coverage probability which can also be inverted into acceptance regions of $H_0:\rho=0$. Example is considered where the HPD interval based on the one-at- a-time reference prior turns out to be the shortest credible interval having the same coverage probability.

In this paper, we propose a method for an exact test of H : $p_i$ = $r_i$ for all i against K : $p_i$ $\neq$ $r_i$ for some i in an unbalanced random effect linear model, where $p_i$ denotes the ratio of the i-th variance component to the error variance. Then we present a method to test H : $p_i$ $\leq$ r against K : $p_i$> r for some specific i by applying orthogonal projection on the model. We also show that any test statistic that follows an F-distribution on the boundary of the hypotheses is equal to the one given here.

In the one-way random effect model, we often estimate the variance components by the ANOVA method and then estimate the population mean. Whe there are only two distint group sizes, the conventional mean estimator is represented as a weighted average of two normal means with weights being the function of variance component estimators. In this paper, we will study a method which can compute the exact variance of the mean estimator when we set the negative variance component estimate to zero.