• The Journal of Asian Finance, Economics and Business
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• v.5 no.4
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• pp.35-44
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• 2018

The study is aimed at investigating the main factors that affect the interest rate yields, in the long-term. In addition, the study surveys the theories and literature relating to the determinants of interest rate. The importance of which is essential not only for governments, but also for banks and corporate financial risk management decisions, including risk exposures in banks and capital markets. Interest rate influences corporate profit as well as growth. For this purpose, the study examines the impact of budget deficit, risk-free rate, capital inflows, money supply and business cycles on real interest rate in Jordan. These factors are based upon well-established theories and straightforward practical view as interest rate determinants. Using data for (1990-2015), the study employed Johansen's co-integrating test, which takes into consideration the long-term unsynchronized relationships. The data is tested for normality, symmetric correlations, covariance diagonal and unit root. The results show that the government budget deficit, short-term risk-free interest rate, capital inflows, money supply and business cycle are long-term determinants of the real interest rate in Jordan. The coefficients of government budget deficit, short-term risk-free rate, money supply and business cycle all are inversely affecting the real interest rate, while capital inflows has a positive impact on the real interest rate.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.845-852
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• 2003

Forecasting exchange rate movements is a challenging task since exchange rates impact world economy and determine value of international investments. In particular, Korean-U.S. exchange rate behavior is very important because of strong Korean and U.S. trading relationship. Neural networks models have been used for short-term prediction of exchange rate movements. Least squares support vector machine (LS-SVM) is used widely in real-world regression tasks. This paper describes the use of LS-SVM for short-term and long-term prediction of Korean-U.S. exchange rate.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• Korea Trade Review
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• v.46 no.6
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.123-129
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• 2020

This scholarly work is an effort to capture the effects of oil prices on the actual exchange rate between dollar and rupee. This is done with reference to the U.S. dollar as oil prices are marked in USD (U.S. Dollar) in the international market, and India is among the top five importers of oil. Using monthly data from January 2001 to May 2020. The study used the real GDP, money supply, short-term interest rate difference between two countries, and inflation apart from the crude oil prices per barrel as the factors that help define the exchange rate. The analysis, through cointegration and vector error correction method (VECM), suggests long and short-run causality amid prices of oil and the rate of exchange fluctuations. Oil prices are found to be negatively related to the exchange rate in the long term but positively related in the short term. The result of the Wald test also indicates the short-run causation from the short-term interest rate and the prices of crude oil towards the exchange rate. The present study shows that oil prices are evidence of the existence of short-term and long-term driving associations with short-term interest rates and exchange rates.

• Health Policy and Management
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• v.21 no.3
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• pp.381-396
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• 2011

This study is to investigate the factors affecting the regional rate of certification for long-term care insurance benefits. Analyzed data were the 253,935 certified beneficiaries (equivalent to 4.9% of total elderly population) as long-term care degree (LTC degree) 1~3 extracted from the applicants for long-term care in the beginning stage of the system from April 15 2008 to July 1 2009. Although the data were collected from individuals, after restructured into regional data and then analysed in the unit of 225 administrative regions for the Korean Long-term Care Insurance. The rate of certification was operated as the percentage of people of LTC degree 1~3 to the elderly population in each region. The average rate of certification among regions was 4.91%, and ranged from 2.20% to 8.32%. In the analysing regression models, most socio-demographic variables, applicants' disease characteristics, regional service infrastructure, and the certification interviewer's characteristics were included. The most influencing variables were the disease factors of applicants, especially dementia or cerebrovascular disease rather than arthritis, osteoporosis, or fracture patients were strong factors for the regional rate of certification. However, advanced studies adding more explainable factors on the regional variance of certification rate would be necessary to provide political agenda and measures for evidence-based certification process with high reliability and validity for a sustainable LTC system in Korea.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.349-360
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• 2023

Dynamic Nelson-Siegel model is widely used in modeling term structure of interest rates for financial products. In this study, we explain dynamic Nelson-Siegel model from the perspective of the state space model and explore Bayesian approaches that can be applied to that model. By applying SOFR term rate data to the Bayesian dynamic Nelson-Siegel model, we confirm the performance and compare it with other competing models such as Vasicek model, dynamic Nelson-Siegel model based on the frequentist approach, and the two-factor Bayesian dynamic Nelson-Siegel model. We also confirm that the Bayesian dynamic Nelson-Siegel model outperformed its competitors on SOFR term rate data based on RMSE.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.213-224
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• 2020

The study assesses the impact of trade liberalization factors on changes in customs revenues in Vietnam. Research data was conducted between 2002 and 2017 on the official website of the Government's Web Portal and The World Bank. This paper uses the vector error correction model to estimate the short-term and long term relationship between data series. The results have proven that tariff reductions have a positive effect on short-term and long-term customs revenues in Vietnam. However, the implementation of other international commitments on trade liberalization has positive short-term and long-term negative impacts on customs revenues in Vietnam. The study's results also show that exchange rate has no effect on changes in customs revenues in the short term but it has a strong impact on increasing customs revenues in the long run. Based on these findings, the article also suggests a number of policies to ensure customs revenues in Vietnam in future. In order to ensure customs revenues, the government of Vietnam should: (1) having some policy to improve the efficiency of customs management in Vietnam; (2) Building appropriate VND exchange rate policy; (3) Establishing reasonable non - tariff barriers to prevent fraud and ovations cause losses in customs revenues.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.135-136
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• 2022

Defects caused by apartment houses have the term of warranty liability according to the enforcement ordinance of Acts of the Management of Apartment Houses. In case when defects occur during the term, free defect maintenance can be provided from the construction company. Yet, there occur conflicts between the construction company and residents, as to whether there occur defects or not. To resolve these conflicts, this study aimed to analyze construction classification and types that need managing, based on defects of apartment houses occurring during the term of warranty liability. This research analyzed 138,576 cases of data, as of five apartment house complexes. For the construction classification for defects of apartment houses, wooden flooring products accounted for the highest rate, followed by paper hanging, and wooden window. For the construction types of defects, torn/scratching took up with the highest rate, followed by the condition of defect in fixing and operating. In order to embody defects occurring during the term of warranty liability, into the visualization technique, this researcher utilized the word cloud method. This study will pursue the method for maintaining defects during the term of warranty liability, in the subsequent research, using the data that this research presented.

• Health Policy and Management
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• v.21 no.4
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• pp.617-642
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• 2011

The purpose of our study is to analyze the determinants of the benefits of the long-term care insurance in Korea using 2008 and 2009 cross-sectional data. Per capita long-term care insurance benefits can be divided into home care services utilization rate, institutional care services utilization rate, per capita home care services benefits, and per capita institutional care services benefits, which are used as the dependent variables in our regression analysis. Admission rate and the ratio of the admitted to the applicant also used as the dependent variables. The results of our analysis show that the explanatory variables such as income level, needs for care, family type, access to the services, and regional characteristics are statistically significant to explain the dependent variables, the long-term care insurance benefits. The higher is the regional income and the more of the female residents, the more are the long-term care insurance benefits. The easier is the access to the services, the more are the insurance benefits. In the rural area, the level of the insurance benefits is relatively high. We propose that copayment rates of the long-term care insurance should be examined and monitoring on the over-use of the services should be done. Also preventive services and care by the family member should be expanded.