• Journal of Korea Port Economic Association
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• v.30 no.4
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• pp.255-273
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• 2014

The purpose of this paper is to examine the causal relationship among the trading volume of Pyeongtaek port, Incheon Inner Harbor, Incheon North Harbor. Methodologically, Granger causality, impulse response function, and variance decomposition based on VAR are used. The results indicate that Pyeongtaek port trading volume positive shock has positive effects on Incheon North Harbor. In addition, Incheon Inner Harbor trading volumes positive shock has negative effects on Pyeongtaek port. The results also suggest that the volume of Pyeongtaek port Granger-causes the volume of Incheon North Harbor, but not vice versa. The volume of Incheon Inner Harbor Granger-causes the volume of Pyeongtaek port. Based on these results, we suggest that port authorities have to focus on policies that would promote copetition between port of Pyeongtaek and Incheon in the world harbor industry.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.265-275
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• 2009

This paper considers a linear regression model with a spatial autoregressive disturbance with ill-posed data and proposes the generalized maximum entropy(GME) estimator of regression coefficients. The performance of this estimator is investigated via Monte Carlo experiments. The results show that the GME estimator provides efficient and robust estimate for the unknown parameter.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.75-83
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• 2016

We propose methodology to analyze the dynamic mechanisms of financial market contagion under market integration using a biological contagion analytical approach. We employ U-statistic to measure market integration, and a dynamic model based on an error correction mechanism (single equation error correction model) and latent factor model to examine market contagion. We also use quantile regression and Wald-Wolfowitz runs test to test market contagion. This methodology is designed to effectively handle heteroscedasticity and correlated errors. Our simulation results show that the single equation error correction model fits well with the linear regression model with a stationary predictor and correlated errors.

• KDI Journal of Economic Policy
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• v.11 no.3
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• pp.39-59
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• 1989

본고(本稿)는 Sims가 개발한 방법을 이용하여 우리나라와 같이 경제구조(經濟構造)가 급히 변하는 상황에서의 경제예측(經濟豫測)의 정확도(正確度)를 제고하고자 하는 시도의 일환이다. 본고(本稿)는 예측자의 사전신뢰(事前信賴)를 이용하여 계수의 값에 대하여 사전제약(事前制約)을 부과(賦課)하고 시간변동(時間變動)을 허용하는 변동계수(變動係數)벡타자귀(自歸)(TBVAR)모형(模型)의 추정방법뿐만 아니라 사전제약(事前制約)의 모수(母數)를 선택하는 방법과 오차(誤差)의 분산(分散)이 자기회귀(自己回歸)할 경우의 대처방법 등 예측(豫測)의 정확도(正確度)를 제고시키는 데 실제 사용되는 방법을 설명하고, 6변수모형(變數模型)을 이용하여 TBVAR 모델의 정확도(正確度)를 타(他) 모델과 비교한다. 정부건설(政府建設), 총통화(總通貨), 사채시장이자율(社債市場利子率), 민간건설(民間建設), 실질(實質)GNP 및 소비자(消費者) 물가지수(物價指數) 등 6변수(變數)에 대한 예측의 정확도를 "타일 U"값을 기준으로 비교할 때 TBVAR은 시간변동(時間變動)을 고려하지 않고 사전제약(事前制約)만 적용한 BVAR이나 사전제약(事前制約)도 적용하지 않은 VAR보다 대부분의 변수의 예측에 있어 더 정확하며 민간건설(民間建設)을 제외하고는 OLS보다 예측오차(豫測誤差)가 작게 나타난다.

• International Area Studies Review
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• v.16 no.1
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• pp.3-27
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• 2012

This paper adopts recently developed panel unit root test that is cross-sectionally robust. Cointegration test is also used to find whether regional house prices are in line with gross regional domestic production (GRDP) in the long run in Korea during 1989-2009. Based on the panel VECM and the panel ARDL models, we examine causal relationships among the variables and estimate the long-run elasticity. We find evidence of cointegration and bidirectional causal relationships between regional house prices and GRDP. The results of long-run estimates, using both fixed effect and ARDL models, show that house prices positively and significantly influence on the GRDP and vice versa. Together with these results, the findings of ARDL-ECM imply that there exists a long-run equilibrium relationship between house prices and regional economic variables even if there is a possibility of short-run deviation from its long-run path.

• Journal of the Korean Geographical Society
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• v.45 no.6
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• pp.806-819
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• 2010

Population data has been essential and fundamental in spatial analysis and commonly aggregated into political boundaries. A conventional method for population distribution estimation was a regression model with land use data, but the estimation process has limitation because of spatial autocorrelation of the population data. This study aimed to improve the accuracy of population distribution estimation by adopting a Regression-Kriging method, namely RK Model, which combines a regression model with Kriging for the residuals. RK Model was applied to a part of Seoul metropolitan area to estimate population distribution based on the residential zones. Comparative results of regression model and RK model using RMSE, MAE, and G statistics revealed that RK model could substantially improve the accuracy of population distribution. It is expected that RK model could be adopted actively for further population distribution estimation.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.803-808
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• 1998

본 논문에서는 국제전화 수요에 영향을 주는 거시경제 변수를 조사하여, 관계를 판명하고, 특히 국제전화 요금이 수요에 미치는 영향을 밝힐 수 있는 요금탄력성 계수를 추정하는 것이 목적이다. 우리는 오차의 자기상관을 고려한 회귀분석 모형을 이용하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.4
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• pp.723-733
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• 2016

This study aims to analyze the effects of urban characteristics on the amount of damage caused by natural disasters. It is focused on the areas of a municipal level in Korea. Also, it takes into account the spatial autocorrelation of the damage caused by natural disasters. Moran's I statistics was estimated to examine the spatial autocorrelation in the damage from the study area. Subsequent to evaluating the suitability for spatial regression models and the OLS regression model, the spatial lag model was employed as an empirical analysis for the study. It showed that the increase in residential area leads to the decrease in the amount of natural disaster damage. On the other hand, the increase in green area and river basin is associated with the increase in the damage. As a result of empirical analysis, appropriate policy establishment and implementation about the damage-adding factors is needed in order to reduce the amount of damage in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1516-1519
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• 2011

예측에 필요한 중요한 자료에는 비선형 자료와 시계열과 같은 선형 자료 등이 있다. 이들 자료는 그 함축적인 관계가 매우 복잡하여 전통적인 통계분석 도구로 식별하는데 어려움이 많다. 신경망 분석은 비모수적 문제나 비선형 곡선 적합능력의 우수성 때문에 현실세계에서의 고유한 복잡성을 다루는 많은 경제 응용 분야에서 널리 이용되고 있다. 신경망은 또한 경제 시계열자료의 예측분야에서도 여러 연구에서 훌륭한 도구로서 적용되고 있다. 전통적으로 우리나라에서 시계열자료의 예측은 선형 자료적 분석을 중심으로 하는 분석도구인 자기회귀누적이동평균(ARIMA)모형을 이용한 시계열분석이 일반적이다. 이 연구에서는 신경망과 ARIMA 모형을 이용하여 한국의 주가변동 자료 및 자동차등록 현황 자료등과 같은 시계열자료를 이용한 예측결과를 비교한다. 연구의 결과는 신경망을 이용한 예측 방법들이 ARIMA 예측 결과보다 통계적으로 작은 오차를 주는 보다 효율적인 방법임을 보여주고 있다.