• Korea Trade Review
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• v.44 no.2
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• pp.51-62
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• 2019

This study predicts the future world seaborne trade volume with econometrics methods using 23-year time series data provided by Clarksons. For this purpose, this study uses simple regression analysis, exponential smoothing method and seemingly unrelated regression model (SUR Model). This study is meaningful in that it predicts worldwide total seaborne trade volume and seaborne traffic in four major items (container, bulk, crude oil, and LNG) from 2019 to 2023 as there are few prior studies that predict future seaborne traffic using recent data. It is expected that more useful references can be provided to trade related workers if the analysis period was increased and additional variables could be included in future studies.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.627-641
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• 2016

In this paper, we consider a seemingly unrelated regression (SUR) model and propose a nonparametric Bayesian approach to SUR with a Dirichlet process mixture of normals for modeling an unknown error distribution. Posterior distributions are derived based on the proposed model, and the posterior inference is performed via Markov chain Monte Carlo methods based on the collapsed Gibbs sampler of a Dirichlet process mixture model. We present a simulation study to assess the performance of the model. We also apply the model to precipitation data over South Korea.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.291-303
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• 2020

This various factors that affect beef consumption behavior between different types of beef such as Hanwoo, Australian, American, and domestic Yukwoo. Previous studies usually used almost ideal demand system (AIDS) model to show the degree of substitution between meats especially domestic and foreign beef. This a real expenditure each individual and to explain what factors affect consumers especially focusing on various beef. Hence, previous studies used shares and prices as key variableshowever, this study use various socio-demographic variables, consumption tendency, satisfaction and importance for beef consumption, purchasing usage and part, etc. This study a seemingly unrelated regression (SUR) model to enhance efficiency of estimates because error terms of four beef consumption equations are correlated. For, an on-line survey was performed Aug. 5 - 14, and we obtained 979 effective samples. The results show that high income group (more than 700 mil. won per month) purchases more beef than other groups. The origin of orders is Hanwoo, Yukwoo, Australian beef, and American beef. A family who member purchases more Yukwoo than other groups. foreign affects beef consumption regardless of its origin. Individuals who think origin and taste prefer Hanwoo. However, individuals who think price prefer Australian beef.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.991-1002
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• 2014

Both Bayesian and maximum likelihood methods are efficient for the estimation of regression coefficients of various Tobit regression models (see. e.g. Chib, 1992; Greene, 1990; Lee and Choi, 2013); however, some researchers recognized that the maximum likelihood method tends to underestimate the disturbance variance, which has implications for the estimation of marginal effects and the asymptotic standard error of estimates. The underestimation of the maximum likelihood estimate in a seemingly unrelated Tobit regression model is examined. A Bayesian method based on an objective noninformative prior is shown to provide proper estimates of the disturbance variance as well as other regression parameters

• Journal of the Economic Geographical Society of Korea
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• v.12 no.2
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• pp.142-158
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• 2009

The purpose of this paper is to identify determinants of regional export in Korea using the interregional input-output table and SUR(Seemingly Unrelated Regression) model. Regional exports are classified into four groups; intraindustry intraregional export, interindustry intraregional export, intraindustry interregional export and interindustry interregional export. Labor productivity, scale economies, market size, and international trade volumes have positively influenced regional exports while the interregional distances having a negative effect on them. These results imply that it is necessary to operate regional strategies to enhance productivities and market size and to reduce transportation and distribution costs for revitalize a regional economy by increasing regional exports.

• The Journal of Fisheries Business Administration
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• v.53 no.1
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• pp.73-84
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• 2022

The purpose of this study is the impact of national fishing port investment and typhoons on fisheries disaster damage. The dependent variables were the amount of damage to fishing ports, fishing boats, fisheries enhancement, external facilities, mooring facilities, functional facilities, fishing port and typhoons. The analysis period is from 2002 to 2018. Since the error term is in a simultaneous correlation, it was efficiently estimated by analyzing it with a seemingly unrelated regression (SUR) method. As a result of the analysis, external facilities have not significance to all models. Investing in mooring facilities increased the amount of damage to fishing ports for five years. Investing in functional facilities reduced the amount of damage to fishing ports and aquaculture over five years. Typhoons have significance to all models, and the amount of damage increased every time a typhoon occurred. Based on these results, as the influence of typhoons increases, it seems necessary to establish preventive measures. Timely investment and maintenance to enable the role and function of national fishing ports are considered important.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.237-245
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• 2003

MDS(multi-dimensional scaling) for preference data is a graphical tool which usually figures out how consumers recognize, evaluate certain products. This article is mainly concerned with an optimal scaling for MDS when target configuration is available. Rotation of axis and SUR(seemingly unrelated regression) methods are employed to get a new configuration which is obtained as close to the target as we can. Methodologies developed here are also illustrated via a real data set.

• Journal of Environmental Policy
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• v.9 no.4
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• pp.53-76
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• 2010

This paper attempts to estimate and compare environmental Kuznets curves (EKC) for$CO_2$ emissions of five regions constituting South Korea. For this, panel data of $CO_2$ emission for these regions are constucted for the period 1990 - 2007. Close inter-dependency among these five regions is considered by using a seemingly unrelated regression (SUR) model. In addition to real per-capita income, price index of energy sources and population dens ity are included as control variables. Results of estimates show the robust existence of EKC's in all these regions. EKC turning points of five regions range between 13.7 and 21.6 million Korean Won, showing a large variation. This difference among regions should be conisidered for the effective implementation of policies targeting the reduction of $CO_2$ emmission. In addition, the increase of energy price is found help reduce the $CO_2$ emmision while the rise of population density seems to lead to the increase of $CO_2$ emission.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.180-191
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• 2020

This study analyzes factors that affect average daily travel time budgets, using the Time Use Survey data from 1999 to 2014 in Seoul. We first developed multivariate regression models for travel time from each year, considering demographic and socio-economic variables as well as non-home activity time. The model results showed that household and personal characteristics and non-home activities significantly affect travel time, and their effects are different over time. In addition, we developed seemingly unrelated regression (SUR) models for time allocation for non-home activity and travel, considering their correlations, and explanatory variables were compared over time. Overall, demographic and socio-economic variables significantly affect travel time as well as non-home activity time.

• Journal of Korean Society of Transportation
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• v.32 no.2
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• pp.83-92
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• 2014

Weather condition is one of the crucial factors affecting travelers' mode choice. Nevertheless, there are numerous indefinite traffic phenomena under various weather conditions. This study was conducted to verify the hypothesis that transit riderships decrease as precipitation increases. To clarify the relationship between precipitation and transit ridership, a seemingly unrelated regression model was employed with data such as daily precipitation and daily transit riderships of 3 transit modes (bus, metro, and shuttle bus) collected in Busan for recent 24 months. The estimation results show that transit riderships decreased as the daily precipitation increased when the daily precipitation is greater or equal to 10mm/day (0.169%, 0.101%, and 0.172% reduction in bus, metro, and shuttle bus riderships, respectively, when the daily precipitation increased by 1mm). When comparing the impact of precipitation on transit riderships by modes using a cross-equation parameter restriction test, the decrease in metro ridership is relatively insensitive to the change in precipitation. However, the negative coefficient of precipitation in the metro ridership estimation model indicates that the transit users in Busan may alter their mode to taxi or automobile and/or may give up the trip itself in bad weather condition.