• Korea Trade Review
• /
• v.42 no.6
• /
• pp.131-154
• /
• 2017

In international trade, transportation generally has the largest and direct impact on freight costs. However, it is also sensitive to external factors such as global economic conditions, global trade volume and exchange rate. Therefore, it is necessary to examine the relationship and influence of international trade in terms of external factors that affect the change of imports and exports by marine and air transportation through empirical analysis. In particular, the analysis of the impact of these external factors on marine and air transportation is an important topic when recent exchange rate changes are significant, and it is also necessary to analyze what transportation means are more sensitive to exchange rate changes. In this study, we use the Vector Error Correction Model to analyze the dynamic effects of changes in exchange rate and domestic and international economic conditions on marine and air transportation from January 2000 to March 2017. Respectively. Alos, Impulse response function and variance decomposition were examined.

• Korean Journal of Agricultural Science
• /
• v.43 no.1
• /
• pp.136-143
• /
• 2016

Garlic and onion are staple agricultural products to Koreans and also are important with regard to agricultural producers' income. These products' acreage responses are highly correlated with each other. Therefore, it is necessary to test whether there is a cointegration relationship between garlic acreage and onion acreage when one tries to estimate the acreage response's function. Based upon the test result of cointegration, it is confirmed that there is no statistically significant cointegration relationship between garlic acreage and onion acreage. In this case, vector autoregressive model is preferred to vector error correction model. This study investigated the dynamic relationship between garlic and onion acreage responses using vector autoregressive (VAR) model. The estimated results of VAR acreage response models show that there is a statistically significant relationship between current and lagged acreage of more than one lag. Therefore, it is recommended that government should consider the long-run period's relationship of each product's acreage when it plans a policy for stabilizing the supply and demand of garlic and onion. For the price variables, garlic price only affects garlic acreage response while onion price affects not only onion acreage response but also garlic acreage response. This implies that the stabilizing policy for onion price could have bigger effects than that for garlic price stabilization.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.415-423
• /
• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• The Korean Journal of Applied Statistics
• /
• v.24 no.2
• /
• pp.227-237
• /
• 2011

This paper considers a generalized method of moments(GMM) estimation for seasonal cointegration as the extension of Kleibergen (1999). We propose two iterative methods for the estimation according to whether parameters in the model are simultaneously estimated or not. It is shown that the GMM estimator coincides in form to a maximum likelihood estimator or a feasible two-step estimator. In addition, we derive its asymptotic distribution that takes the same form as that in Ahn and Reinsel (1994).

• Journal of the Korean Operations Research and Management Science Society
• /
• v.28 no.1
• /
• pp.11-23
• /
• 2003

This study Investigates the short and long-run dynamic relationships between the domestic and U.S. stock markets for the period of declining stock prices. It Is well known that the domestic stock market variations are largely caused by the U.S. stock market movements. Multivariate causal tty test Is utilized to examine the lead-lag relationships among four stock prices of KOSPI and KOSDAQ In the domestic part and DOWJONES and NASDAQ In the U.S. part. When the stock prices tend to decrease In the long run, It Is found that both KOSPI and KOSDAQ have closer relations with NASDAQ than DOWJONES. When both of domestic stock markets are severely fluctuate, bidirectional causal relationships appear to exist between NASDAQ and each of KOSPI and KOSDAQ. On the other hand. when the domestic stock markets are relatively stable, unidirectional causality Is found to exist between NASDAQ and each of KOSPI and KOSDAQ. which is explicitly validated by the analysis of variance decomposition.

• The Korean Journal of Applied Statistics
• /
• v.25 no.6
• /
• pp.925-932
• /
• 2012

The article introduces a method to estimate a cointegrated vector autoregressive model, using mixed-frequency data, in terms of a state-space representation of the vector error correction(VECM) of the model. The method directly estimates the parameters of the model, in a state-space form of its VECM representation, using the available data in its mixed-frequency form. Then it allows one to compute in-sample smoothed estimates and out-of-sample forecasts at their high-frequency intervals using the estimated model. The method is applied to a mixed-frequency data set that consists of the quarterly real gross domestic product and three monthly coincident indicators. The result shows that the method produces accurate smoothed and forecasted estimates in comparison to a method based on single-frequency data.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.11
• /
• pp.127-135
• /
• 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.

• Journal of Information Processing Systems
• /
• v.15 no.2
• /
• pp.305-319
• /
• 2019

The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_2
• /
• pp.1591-1604
• /
• 2023

In ocean color remote sensing, atmospheric correction is a vital process for ensuring the accuracy and reliability of ocean color products. Furthermore, in recent years, the remote sensing community has intensified its requirements for understanding errors in satellite data. Accordingly, research is currently addressing errors in remote sensing reflectance (R_rs) resulting from inaccuracies in meteorological variables (total ozone, pressure, wind field, and total precipitable water) used as auxiliary data for atmospheric correction. However, there has been no investigation into the error in R_rs caused by the variability of the water vapor profile, despite it being a recognized error source. In this study, we used the Second Simulation of a Satellite Signal Vector version 2.1 simulation to compute errors in water vapor transmittance arising from variations in the water vapor profile within the GOCI-II observation area. Subsequently, we conducted an analysis of the associated errors in ocean color products. The observed water vapor profile not only exhibited a complex shape but also showed significant variations near the surface, leading to differences of up to 0.007 compared to the US standard 62 water vapor profile used in the GOCI-II atmospheric correction. The resulting variation in water vapor transmittance led to a difference in aerosol reflectance estimation, consequently introducing errors in R_rs across all GOCI-II bands. However, the error of R_rs in the 412-555 nm due to the difference in the water vapor profile band was found to be below 2%, which is lower than the required accuracy. Also, similar errors were shown in other ocean color products such as chlorophyll-a concentration, colored dissolved organic matter, and total suspended matter concentration. The results of this study indicate that the variability in water vapor profiles has minimal impact on the accuracy of atmospheric correction and ocean color products. Therefore, improving the accuracy of the input data related to the water vapor column concentration is even more critical for enhancing the accuracy of ocean color products in terms of water vapor absorption correction.

• The Korean Journal of Applied Statistics
• /
• v.24 no.5
• /
• pp.791-797
• /
• 2011

This paper considers the issue of seasonal cointegrating rank selection by information criteria as the extension of Cheng and Phillips (2009). The method does not require the specification of lag length in vector autoregression, is convenient in empirical work, and is in a semiparametric context because it allows for a general short memory error component in the model with only lags related to error correction terms. Some limit properties of usual information criteria are given for the rank selection and small Monte Carlo simulations are conducted to evaluate the performances of the criteria.