• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.69.2-69
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• 2002

Process measurements contain random and gross errors and the size estimation of gross errors is required for production accounting. Mixed integer programming technique had been applied to identify and estimate the gross errors simultaneously. However, the compensate model based on mixed integer programming used all measured variables or spanning tree as gross error candidates. This makes gross error estimation problem combinatorial or computationally expensive. Mixed integer programming with test statistics is proposed for computationally inexpensive gross error identification /estimation. The gross error candidates are identified by measurement test and the set of gross error candidates are...

• 한국측량학회지
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• 제4권2호
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• pp.88-93
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• 1986

선형모델의 최소제곱조정에서 관측값에 포함된 과대오차는 상관관계가 있는 다른 잔차들에 영향을 준다. 따라서 표준화잔차를 기초로 하는 Baarda의 방법은 수정되고 변화되어 왔다. 본 연구에서는 다 과대오차를 제거하기 위해 발표된 방법들을 비교분석하고, 상좌표의 과대오차제거에 적용하는데 목적을 두고 있다.

• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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• pp.127-135
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• 2020

This study aims to measure the effects of real interest rate (RIR), gross domestic savings (GDS), and net exports (EN) shocks on Indonesia's economic growth (EG). The focus on Indonesia is unique due to the abundant resources available in the nation, but they are unsuccessful in boosting economic growth. This study applied a quantitative method to comprehensively analyze the correlation between variables by employing Vector Autoregression Model (VAR) combined with Vector Error Correction Model (VECM). Various procedures are preformed: Augmented Dickey-Fuller test (ADF), Optimum Lag Test, Johansen Cointegration Test, Granger Causality Test, as well as Impulse Response Function (IRF) and Error Variance Decomposition Analysis (FEVD). The data were collected from the World Bank and the Asian Development Bank from 1986 to 2017. The findings of the study indicated that economic growth responded positively to real interest rate shocks, which implies that when the real interest rate experiences a shock (increase), the economy will be inclined to growth. While, economic growth responded negatively to gross domestic savings and net export shocks. Policymakers are expected to consider several matters, particularly the economic conditions at the time of formulating policy, so that the prediction effectiveness of a policy can be appropriately assessed.

• 아태비즈니스연구
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• 제10권2호
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• pp.13-26
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• 2019

This paper aims to empirically examine the short-run and long-run aggregate demand for the US imports using quarterly economic data for the period 2000-2018 including aggregate imports, final expenditure components, gross fixed capital formation and relative price of imports. According to the results of both multivariate co-integration analysis and error correction model, the above variables are all cointegrated and significant differences are found to exist among the long-run partial elasticities of imports as regards different macro components of final expenditure. Partial elasticities with respect to government expenditure, gross fixed capital formation, exports and relative price of import are found to be positive while imports seems to respond negatively to changes in private consumption, implying that an increase in private consumption could result in a significant reduction in demand for imports in the long run. With regard to the relative import prices, the results appear to indicate a relatively insignificant influence on the aggregate imports in the US in the long run. However, an error correction model designed for predicting the short-term variability shows that only exports have an impact on the imports in the short run.

• 환경영향평가
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• 제29권4호
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• pp.272-285
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• 2020

본 연구에서는 3차원 기상 및 대기질 모델의 입출력 자료를 평가하는 데 필요한 통계 검증지표를 선별하고, 선정된 검증지표의 기준치를 조사하여 그 결과를 요약하였다. 여러 국내외 문헌과 최근 논문 검토를 통해 최종 선정된 통계 검증지표는 MB (Mean Bias), ME (Mean Error), MNB (Mean Normalized Bias Error), MNE (Mean Absolute Gross Error), RMSE (Root Mean Square Error), IOA (Index of Agreement), R (Correlation Coefficient), FE (Fractional Error), FB (Fractional Bias)로 총 9가지이며, 국내외 문헌을 통해 그 기준치를 확인하였다. 그 결과, 기상모델의 경우 대부분 MB와 ME가 주요 지표로 사용되어 왔고, 대기질 모델 결과는 NMB와 NME 지표가 주로 사용되었으며, 그 기준치의 차이를 분석하였다. 아울러 이들 통계 검증지표값을 이용하여 모델 예측 결과를 효과적으로 비교하기 위한 표출 도식으로 축구 도식, 테일러 도식, Q-Q (Quantile-Quantile) 도식의 장단점을 분석하였다. 나아가 본 연구 결과를 기반으로 우리나라의 산악지역의 특수성 등이 잘 고려된 통계 검증지표의 기준치 설정 등의 추가연구가 효과적으로 진행될 수 있기를 기대한다.

• 지역연구
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• 제35권1호
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• pp.47-57
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• 2019

본 연구의 목적은 계량분석을 통해 우리나라 지역내총생산에 영향을 미치는 주요 요인별 영향력을 분석하는 것이다. 본 연구는 분석대상 지역으로 대구광역시를 선정했는데 대구광역시는 영남 지역의 중추기능을 지속적으로 수행해 왔으며, 우리나라 섬유 화학제품 등의 수출 전략기지로 지위하고 있다. 또한 영남 지역에 도달하는 주요 수입화물의 기종점 역할을 수행하는 등 이처럼 대구 지역은 우리나라 수출입 무역 확대와 국가경제 성장에 기여하는 바가 높다. 이를 위해 본 연구는 공적분모형(Co-integration Model)과 벡터오차수정모형(Vector Error Correction Model; VECM)을 사용하여 대구 지역내총생산에 영향을 미치는 장기균형함수를 추정하였다. 본 연구는 우리나라 지역내총생산에 영향을 미치는 주요 요인들의 영향력을 정량적인 방식을 통해 추정하고 장기 균형 시점의 총생산으로부터 괴리가 발생했을 때 얼마나 빠른 속도로 장기균형으로 수렴하는가를 추정하였다는 점에서 의의가 있다.

• The Journal of Asian Finance, Economics and Business
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• 제8권5호
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• pp.37-41
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• 2021

This study aims to investigate the relationship between the exchange rate and Trade Balance. Trade Balance is used as the dependent variable, and the independent variables are Exchange Rate, Gross Domestic Product, and Inflation. Augmented Dickey-Fuller unit root test was adopted to test the stationary property of time series data, Auto Regressive Distributed Lag model was employed to find the long run and short-run relationship and long-run adjustment, Bound test approach, the unrestricted Error Correction Model and Granger Causality Test are used to analyze the data from 1977 to 2019. The research findings suggest that inflation has a positive impact on the trade balance in the short run. The exchange rate and the Gross Domestic Product have adverse effects on Trade balance in the long run. The coefficient of ER in the previous year is negative, and the coefficient of TB in the previous year is positive and significant. This is consistent with the J-Curve phenomenon, which states that devaluation may not improve trade balance in the immediate period, but will significantly impact the trade balance improvement in subsequent periods. Hence Marshall Lerner Condition exists in Sri Lanka.

• 한국농공학회논문집
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• 제52권6호
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• pp.1-7
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• 2010

This study was carried out to investigate the effect of hydro power factors (e.g., irrigation area, watershed area, active storage, gross head) on annual generation capacity and operation ratio for agricultural reservoirs in Chungbuk Province with active storage of over 1 million $m^3$. The annual generation capacity and operation ratio were estimated using HOMWRS (Hydrological Operation Model for Water Resources System) from last 10-year daily hydrological data. The correlation coefficients between annual generation capacity and the hydro power factors except gross head were high (over 0.87), but the correlation coefficients between operational rate and the factors were low (below 0.28). The optimum multiple regression equations of the annual generation capacity were expressed as the functions of watershed area, active storage, and gross head. Also, the simple regression equation of annual generation capacity was expressed as a function of watershed area. The average relative root-mean-square-error (RRMSE) between observed and estimated values by the optimum multiple regression equations was smaller than that by the simple regression equation, suggesting that the former has more accuracy than the latter.

• Journal of the Korean Statistical Society
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• 제27권1호
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• pp.113-132
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• 1998

In this paper, we consider a minimum distance (M.D.) estimation based on kernels for U-statistics. We use Cramer-von Mises type distance function which measures the discrepancy between U-empirical distribution function(d.f.) and modeled d.f. of kernel. In the distance function, we allow various integrating measures, which can be finite, $\sigma$-finite or discrete. Then we derive the asymptotic normality and study the qualitative robustness of M. D. estimates.

• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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• pp.109-114
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• 2020

The aim of this study is to develop basic artificial neural network models in forecasting the in-sample gross domestic product (GDP) of Malaysia. GDP is one of the main indicators in presenting the macro economic condition of a country as set by the world authority bodies such as the World Bank. Hence, this study uses an artificial neural network-based approach to make predictions concerning the economic growth of Malaysia. This method has been proposed due to its ability to overcome multicollinearity among variables, as well as the ability to cope with non-linear problems in Malaysia's growth data. The selected inputs and outputs are based on the previous literatures as well as the economic growth theory. Therefore, the selected inputs are exports, imports, private consumption, government expenditure, consumer price index (CPI), inflation rate, foreign direct investment (FDI) and money supply, which includes M1 and M2. Whilst, the output is real gross domestic product growth rate. The results of this study showed that the neural network method gives the smallest value of mean error which is 0.81 percent with a total difference of 0.70 percent. This implies that the neural network model is appropriate and is a relevant method in forecasting the economic growth of Malaysia.