• Journal of Distribution Science
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• v.11 no.4
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• pp.81-88
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• 2013

Purpose - This paper analyzes the influence of the RMB internationalization on the KRW/dollar exchange rate using an autoregressive distributed lag model. Comparing the parameter estimators from the sample period before and after the global financial crisis, we found that the RMB/dollar exchange rate has increasingly become more influential on the KRW/dollar exchange rate. Moreover, for the past several years, the Chinese government has actively utilized the financial service FTA negotiation as a measure for the RMB internationalization. This paper simultaneously considers RMB internationalization and financial service negotiations in the Korean-Chinese FTA. The purpose of this paper is to explicitly suggest a direction for the financial service negotiations in the Korean-Chinese FTA considering the effects of RMB internationalization. Research design, data, and methodology - The research plan of this paper has two parts. First, for an empirical study, this paper uses the daily exchange rate of the U.S. dollar against the currencies of the ASEAN5, Taiwan,and Korea. By using an autoregressive distributed lag model, this paper studies the influence of the change in the RMB/dollar exchange rate on changes in the local currency/dollar exchange rate in seven economies neighboring China. Our sample periods are 06/2005 - 07/2008 and 06/2010 -02/2013. During these periods, China was under the multi-currency basket system. We exempted the period of 08/2008 - 05/2010 from the analysis because there was nearly no RMB/dollar exchange rate fluctuation during those months. Second, after analyzing the recent financial service liberalizations and deregulations in China, we recommend a direction for the financial service negotiations in the Korean-Chinese FTA. In the past several years,the main Chinese financial policy agenda has surrounded the RMB internationalization. Therefore, it is crucial to understand this in the search for strategies for the financial service negotiations in the Korean-Chinese FTA. This paper employs an existing literature survey and examines the FTA protocols in its research methodology. Results and Conclusions - After the global financial crisis, the Chinese government wanted to break away from the dollar influence and pursued independent RMB internationalization in order to continue the growth and stability of its economy. Hence, every neighboring economy of China has been strategically impacted by RMB internationalization. Nevertheless, there is little empirical study on the influence of RMB internationalization on the KRW/dollar exchange rate. This paper is one of the few studies to analyze this problem comprehensively. By using a relatively simple estimation model, we can confirm that the coefficient of the RMB/dollar exchange rate has become more significant, except in the case of Indonesia. Although Korea is not under the multi-currency basket system but under the weakly controlled floating exchange rate system, its coefficient appears as large as that of the ASEAN5. This is the basis of the currency cooperation that has grown from the expansion of trade between the two countries. These empirical results suggest that the Korean government should specifically consider the RMB internationalization in the Korean-Chinese FTA negotiations.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.25-33
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• 2016

In order to visualize point based Location-Based Social Network Services(LBSNS) data on multi-scaled tile map effectively, it is necessary to apply tile-based clustering method. Then determinating reasonable numbers and size of tiles is required. However, there is no such criteria and the numbers and size of tiles are modified based on data type and the purpose of analysis. In other words, researchers' subjectivity is always involved in this type of study. This is when Modifiable Areal Unit Problem(MAUP) occurs, that affects the results of analysis. Among LBSNS, geotagged Twitter data were chosen to find the influence of MAUP in scale effects perspective. For this purpose, the degree of spatial autocorrelation using spatial error model was altered, and change of distributions was analyzed using Morna's I. As a result, positive spatial autocorrelation showed in the original data and the spatial autocorrelation was decreased as the value of spatial autoregressive coefficient was increasing. Therefore, the intensity of the spatial autocorrelation of Twitter data was adjusted to five levels, and for each level, nine different size of grid was created. For each level and different grid sizes, Moran's I was calculated. It was found that the spatial autocorrelation was increased when the aggregation level was being increased and decreased in a certainpoint. Another tendency was found that the scale effect of MAUP was decreased when the spatial autocorrelation was high.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.61-74
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• 2019

In this study, the SPI (Standardized Precipitation Index) of meteorological drought and SDI (Streamflow Drought Index) of hydrological drought for 1, 3, 6, 9, and 12 months duration were estimated to analyse the characteristics of drought using rainfall and dam inflow data for Chungju dam ($6,661.8km^2$) with 31 years (1986-2016) and Boryeong dam ($163.6km^2$) watershed with 19 years (1998-2016) respectively. Using the estimated SPI and SDI, the drought forecasting was conducted using seasonal autoregressive integrated moving average (SARIMA) model for the 5 durations. For 2016 drought, the SARIMA had a good results for 3 and 6 months. For the 3 months SARIMA forecasting of SPI and SDI, the correlation coefficient of SPI3, SPI6, SPI12, SDI1, and SDI6 at Chungju Dam showed 0.960, 0.990, 0.999, 0.868, and 0.846, respectively. Also, for same duration forecasting of SPI and SDI at Boryeong Dam, the correlation coefficient of SPI3, SPI6, SDI3, SDI6, and SDI12 showed 0.999, 0.994, 0.999, 0.880, and 0.992, respectively. The SARIMA model showed the possibility to provide the future short-term SPI meteorological drought and the resulting SDI hydrological drought.

• Smart Structures and Systems
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• v.11 no.1
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• pp.1-17
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• 2013

This paper intended to investigate the feasibility of bridge health monitoring using a linear system parameter of a time series model identified from traffic-induced vibrations of bridges through a laboratory moving vehicle experiment on scaled model bridges. This study considered the system parameter of the bridge-vehicle interactive system rather than modal ones because signals obtained under a moving vehicle are not the responses of the bridge itself but those of the interactive system. To overcome the shortcomings of modal parameter-based bridge diagnosis using a time series model, this study considered coefficients of Autoregressive model (AR coefficients) as an early indicator of anomaly of bridges. This study also investigated sensitivity of AR coefficients in detecting anomaly of bridges. Observations demonstrated effectiveness of using AR coefficients as an early indicator for anomaly of bridges.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.99-110
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• 2021

This study aims to examine empirically the industry and economic fundamental factors that affect the stock prices of the leading palm oil company in Indonesia. The dynamics of stock price are analyzed using the autoregressive distribution lag (ARDL) model both for symmetric and asymmetric effects. The data used in this study are monthly data for the period from 2008:01 to 2020:03. In the long run, the company stock price moves in line with the competitor company stock price at the current time. The palm oil price has a positive effect on the stock price. Meanwhile, inflation negatively affects the stock price in the short run. The estimated equilibrium correction coefficient indicates a reasonably quick correction of the distortion of the stock price equilibrium in monthly dynamics. However, fundamental factors have asymmetric effects, especially the response of stock price when these factors decrease rather than increase in the short run. Stock prices that are responsive to declines in fundamental performance should be of particular concern to both investors and management in their strategic decision making. The results of this study will contribute to the enrichment of literature related to stock prices from the viewpoint of economic analysis on firm-level data.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.245-252
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• 2022

This study examines the strength of the impact of fiscal policy tools on economic wellbeing as measured by per capita income in Malaysia from 1996 to 2020. The impact of fiscal policy instruments on economic wellness, represented by real income per capita, is measured using the autoregressive distributed lags model. The speed of adjustment from short-run disequilibrium to long-run equilibrium is also measured to assess the strength of the fiscal instruments' impact on per capita income. Empirical results exhibit the existence of co-integration relationships between per capita income, tax revenue, and government spending. The findings provide strong support for the presence of a long-run positive impact on government spending and a long-run negative impact of tax revenue on per capita income. The coefficient of ECT_t-1 indicates that deviations from a short-run disequilibrium to a long-run equilibrium from the current to the future period are corrected with a speed of 76% (equivalent to a duration of 1.5-2 years to return to equilibrium). The practical and policy implication of the results is fiscal instruments play a significant role, mainly in alleviating the economic impact of the COVID-19 pandemic in the long run.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1664-1673
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• 2022

Achieving sustainable development requires an increasing share of green technologies. World energy demand is expected to rise significantly especially in developing economies. The increasing energy demands will be entertained with conventional energy sources at the cost of higher emissions unless eco-friendly technologies are used. This study examines the asymmetric effects of nuclear energy on carbon emissions for Pakistan from 1974 to 2019. Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) unit root tests suggest that variables are integrated of order one and bound test of Autoregressive Distributed Lag (ARDL) and nonlinear ARDL confirm a long-run relationship among selected variables. The ARDL, Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS) results show that the coefficient of nuclear energy has a negative and significant impact on emissions in both short and long run. Further, the NARDL finding shows that there exists an asymmetric long-run association between nuclear energy and CO₂ emissions. The vector error correction method (VECM) results indicate that there exists a bidirectional causal relationship between nuclear energy and carbon emissions in both the short and long run. Additionally, the impact of nuclear energy on ecological footprint has been examined and our findings remain robust.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1657-1663
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• 2022

This study uses data from 1970 to 2016 to analyze the effect of nuclear energy use on CO₂ emissions and attempts to validate the EKC hypothesis using the Fourier Autoregressive Distributive Lag model in India for the first time. Because of India's rapidly rising population, the environment is being severely strained. However, with 22 operational nuclear reactors, India boasts tremendous nuclear energy potential to cut down on CO₂ emissions. The EKC is validated in India as the significant coefficients of GDP and GDP.² The short-run estimates also suggest that most environmental externalities are corrected within a year. Given the findings, some policy recommendations abound. The negative statistically significant coefficient of nuclear energy consumption is an indication that nuclear power expansion is essential to achieving clean and sustainable growth as a policy goal. Also, policymakers should enact new environmental laws that support the expansion and responsible use of nuclear energy as it is cleaner than fossil fuels and reduces the cost and over-dependence on oil, which ultimately leads to higher economic growth in the long run. Future research should consider studying the nonlinearities in the nuclear energy-CO₂ emissions nexus as the current study is examined in the linear sense.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.529-545
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• 2023

This paper introduces the Banded-VHAR model suitable for high-dimensional long-memory time series with band structure. The Banded-VHAR model has nonignorable correlations only with adjacent dimensions due to data features, for example, geographical information. Row-wise estimation method is adapted for fast computation. Also, two estimation methods, namely BIC and ratio methods, are proposed to estimate the width of band. We demonstrate asymptotic consistency of our proposed estimation methods through simulation study. Real data applications to pm2.5 and apartment trading volume substantiate that our Banded-VHAR model outperforms traditional sparse VHAR model in forecasting and easy to interpret model coefficients.

• Journal of Information Technology Applications and Management
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• v.30 no.3
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• pp.1-13
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• 2023

Demand forecasting is a crucial task for an online retail where has to manage daily fresh foods effectively. Failing in forecasting results loss of profitability because of incompetent inventory management. This study investigated the optimal performance of different forecasting models for a very short shelf-life product. Demand data of 13 perishable items with aging of 210 days were used for analysis. Our comparison results of four methods: Trivial Identity, Seasonal Naïve, Feed-Forward and Autoregressive Recurrent Neural Networks (DeepAR) reveals that DeepAR outperforms with the lowest MAPE. This study also suggests the managerial implications by employing coefficient of variation (CV) as demand variation indicators. Three classes: Low, Medium and High variation are introduced for classify 13 products into groups. Our analysis found that DeepAR is suitable for medium and high variations, while the low group can use any methods. With this approach, the case can gain benefit of better fill-rate performance.