• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6B
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• pp.390-401
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• 2007

This paper is to estimate and analyze the price elasticities of demand for mobile calls. We used the data for the period from January 2000 to December 2005 on a monthly basis. Data used are call minutes to mobile-originating(ML+MM), tariff for dispatch of fixed and mobile calls($P_L,P_M$), income(Y), and subscriber for mobile(N). In order to provide robust estimates of price elasticities, we have used two different econometric models. One is a Dynamic model which includes a lagged dependent variable and so can differentiate between long-un and short-run price elasticities using the Generalized Method of Moments(GMM). The other is a Box-Cox transformation model which is one of the most useful methods. Box-Cox transformation model shows that elasticity changes with the lapse of time. The results are as follow : Not including the price indices for land-originating, the estimate is overestimated otherwise. In Box-Cox transformation case, price elasticity had been steadily declining. And this result shows that mobile services had been changed necessities increasingly in Korea.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.159-167
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• 2000

The purpose of this study is to estimate the stochastic monthly runoff model for the Kunwi south station of Wi-stream basin in Nakdong river system. This model was based on the theory of Box-Jenkins multiplicative ARlMA and the state-space model to simulate changes of monthly runoff. The forecasting monthly runoff from the pair of estimated effective rainfall and observed value of runoff in the uniform interval was given less standard error then the analysis only by runoff, so this study was more rational forecasting by the use of effective rainfall and runoff. This paper analyzed the records of monthly runoff and effective rainfall, and applied the multiplicative ARlMA model and state-space model. For the P value of V AR(P) model to establish state-space theory, it used Ale value by lag time and VARMA model were established that it was findings to the constituent unit of state-space model using canonical correction coefficients. Therefore this paper confirms that state space model is very significant related with optimization factors of VARMA model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.64-78
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• 2019

This paper explores key factors affecting taxi travel using global positioning system(GPS) data in Seoul, Korea, considering spatial dependence. We first analyzed the travel characteristics of taxis such as average travel time, average travel distance, and spatial distribution of taxi trips according to the time of the day and the day of the week. As a result, it is found that the most taxi trips were generated during the morning peak time (8 a.m. to 9 a.m.) and after the midnight (until 1 a.m.) on weekdays. The average travel distance and travel time for taxi trips were 5.9 km and 13 minutes, respectively. This implies that taxis are mainly used for short-distance travel and as an alternative to public transit after midnight in a large city. In addition, we identified that taxi trips were spatially correlated at the traffic analysis zone(TAZ) level through the Moran's I test. Thus, spatial regression models (spatial-lagged and spatial-error models) for taxi trips were developed, accounting for socio-demographics (such as the number of households, the number of elderly people, female ratio to the total population, and the number of vehicles), transportation services (such as the number of subway stations and bus stops), and land-use characteristics (such as population density, employment density, and residential areas) as explanatory variables. The model results indicate that these variables are significantly associated with taxi trips.

• Journal of Environmental Policy
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• v.13 no.1
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• pp.25-47
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• 2014

We examined the heterogeneity in the financial -environmental performance nexus, carrying out a meta-analysis of 48 outcomes from 26 empirical studies. Multiple correspondence analysis (MCA) was performed in this study to facilitate the analysis of the structural relationship among an array of study characteristics. As expected, the results of analyzing the multiple studies of the general corporate environmental performance and financial performance link suggested a significant positive relationship. Some of the results of the moderator analysis suggest that empirical studies using self-reporting measurement and structural equation method benefited from environmental performance as much as or more than the archival and regression method.

• International Area Studies Review
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• v.20 no.2
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• pp.47-62
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• 2016

This paper is to set up the empirical model in order to estimate industrial markup and to analyze the determinants for industrial markup by estimating the factor price elasticities of markup in the Korean manufacturing industries using the autoregressive distributed model. The import price elasticities of markup were estimated to be -1.025, -0.176, and -0.260 respectively in Machinery products, Chemical products, and Metallics which proved to have higher ratios of imported intermediate goods to industrial output. The interest elasticities of markup were also estimated to be -0.165, -0.147, and -0.210 respectively in Chemical products, Metallics, and Machinery products which are capital-intensive industries. Thus, the paper suggests that both import price index and interest rate have had more decisive effects on the changes in industrial markup in the Korean manufacturing industries, in particular, since the foreign currency crisis beginning in late 1997.

• Journal of Energy Engineering
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• v.22 no.4
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• pp.370-375
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• 2013

This paper attempts to estimate the city gas demand function in Korea over the period 1981-2012. As the city gas demand function provides us information on the pattern of consumer's city gas consumption, it can be usefully utilized in predicting the impact of policy variables such as city gas price and forecasting the demand for city gas. We apply lagged dependent variable model and ordinary least square method as a robust approach to estimating the parameters of the city gas demand function. The results show that short-run price and income elasticities of the city gas demand are estimated to be -0.522 and 0.874, respectively. They are statistically significant at the 1% level. The short-run price and income elasticities portray that demand for city gas is price- and income-inelastic. This implies that the city gas is indispensable goods to human-being's life, thus the city gas demand would not be promptly adjusted to responding to price and/or income change. However, long-run price and income elasticities reveal that the demand for city gas is price- and income-elastic in the long-run.

• Economic Analysis
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• v.25 no.3
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• pp.100-137
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• 2019

In this study, I analyzed the effects of government spending on macro variables and on each industry by using a factor augmented vector autoregressive model (FAVAR) and 167 macro-variables in Korea since 2000. The results reveal that the effects of two types of government spending - government consumption and government investment - greatly differ, therefore it is better to consider the two types of spending separately for a more precise analysis. The stimulus effects of government consumption are clear, but those of government investment are not. In addition, the crowding-out effects of government spending take place through the current account deficit channel rather than the traditional crowding-out channel, reducing private consumption and investment. Both types of government spending show a positive effect on the construction industry. Also, an increase in government consumption stimulates output in various manufacturing and service sectors.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.873-885
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• 2016

Onions are grown in a few specific regions of Korea that depend on the climate and the regional characteristic of the production area. Therefore, when onion yields are to be estimated, it is reasonable to use a statistical model in which both the climate and the region are considered simultaneously. In this paper, using a spatial panel regression model, we predicted onion yields with the different weather conditions of the regions. We used the spatial auto regressive (SAR) model that reflects the spatial lag, and panel data of several climate variables for 13 main onion production areas from 2006 to 2015. The spatial weight matrix was considered for the model by the threshold value method and the nearest neighbor method, respectively. Autocorrelation was detected to be significant for the best fitted model using the nearest neighbor method. The random effects model was chosen by the Hausman test, and the significant climate variables of the model were the cumulative duration time of sunshine (January), the average relative humidity (April), the average minimum temperature (June), and the cumulative precipitation (November).

• The Korean Journal of Financial Studies
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• v.5 no.1
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• pp.213-236
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• 1999

최근 우리 나라는 금융환경의 변화가 진전됨에 따라 보험산업에도 변화가 일어나기 시작했다. 이에 따라 보험산업은 지급능력 및 수익성에 관심을 갖게 되었다. 이에 본 연구에서는 국내 생명보험회사의 투자수익율이 재무제표에 나타난 요인에 의해 어떻게 결정되는가를 살펴봄으로써 수익률 결정요인을 찾는데 있다. 본 연구에서 사용한 자료는 생명보험회사 33개사 중에서 외국사를 제외한 29개사를 선택하여 수집하였다. 분석 기간은 1989년부터 1996년까지이며, 생명보험회사는 기존사, 지방사, 내국사, 합작사로 구분하였다. 분석결과, 시차별 분석에서는 결정계수가 기간이 짧을수록 높게 나타났고 예측된 부호는 잉여금, 사업비율이 반대로 나타났다. 그룹별 분석에서는 기존사, 내국사, 지방사, 합작사의 모델이 각각 유의수준 5%에서 유의하였고 결정계수는 높게 나타났다. 예측부호는 자산증가율과 사업비율, 수입보험료 증가율(기존사 제외), 부채/자본비율(기존사 제외)이 일치하지 않았다. 경영평가제도에 의한 분석에서는 결정계수가 높은 편이며, 유의수준 5%에서 유의하였다. 자본증가율은 예측된 부호와 일치하나 영향력이 거의 없는 것으로 나타났다. 유동성 비율은 신설사(내국사, 지방사, 합작사)가 예측부호와 반대의 경우로 나타났다. 또한 총자산은 투자수익율과 규모에 의해 결정되지 않은 것으로 나타났다. 모집인은 투자수익율에 유의적이나 직접적인 투자요인이 아닌 것으로 분석되었다. 기존연구와 비교해 볼 때, 한국 생명보험회사의 잉여금과 효력상실 해약율은 기존연구 모형과 예측부호가 일치하나 나머지 변수는 그룹간 다소 상이하게 나타났다. 결론적으로 본 연구의 분석 결과, 예측부호는 다소 차이가 있는 것으로 나타났고, 유의적인 변수는 없는 것으로 분석된다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1427-1435
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• 2017

The smart farm is a remarkable system because it utilizes information and communication technologies in agriculture to bring high productivity and excellent qualities of crops. It automatically measures the growth environment of the crops and accumulates huge amounts of environmental information in real time growing in smart farms using multi-variable control of environmental factors. The statistical model using the collected big data will be helpful for decision making in order to control optimal growth environment of crops in smart farms. Using data collected from a smart farm of tomato, we carried out multiple regression analysis to determine the relationship between yield and environmental factors and to predict yield of tomato. In this study, appropriate parameter modification was made for environmental factors considering tomato growth. Using these new factors, we fit the model and derived the optimal environmental factors that affect the yields of tomato. Based on this, we could predict the yields of tomato. It is expected that growth environment can be controlled to improve tomato productivities by using statistical model.