• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.219-228
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• 2020

The study investigates the impact of inflation rate on economic growth to find the best-fit model for economic growth in Vietnam. The study applied Vector Autoregressive (VAR), cointegration models, and unit root test for the time-series data from 1996 to 2018 to test the inflation impact on the economic growth in the short and long term. The study showed that the two variables are stationary at lag first difference I(1) with 1%, 5% and 10%; trace test indicates two cointegrating equations at the 0.05 level, the INF does not granger cause GDP, the optimal lag I(1) and the variables are closely related as R2 is 72%. It finds that the VAR model's results are the basis to perform economic growth; besides, the inflation rate is positively related to economic growth. The results support the monetary policy. This study identifies issues for Government to consider: have a comprehensive solution among macroeconomic policies, monetary policy, fiscal policy and other policies to control and maintain the inflation and stimulate growth; set a priority goal for sustainable economic growth; not pursue economic growth by maintaining the inflation rate in the long term, but take appropriate measures to stabilize the inflation at the best-fitted VAR forecast model.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.47 no.2
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• pp.99-107
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• 2011

The relationships among long-term variation in catches of Spanish mackerel (Scomberomorus niphonius) and main food organism such as common mackerel (Scomber japonicus), anchovy (Engraulis japonicus), and oceanic condition in Korean waters were analyzed using 40 years of time-series data from 1971－2010. In the 1990s, oceanic conditions around the Korean peninsula shifted to a warmer regime with higher SST (sea surface temperature). The total catch of Spanish mackerel in Korean waters increased dramatically since the early 2000s, and main fishing ground form into South Sea in winter season from December to January. From the results of correlation analysis, we found a significant relationship between the Spanish mackerel catch and environmental factor such as SST, common mackerel and anchovy catch in Korean waters.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.11-19
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• 2021

This study aims to analyze the causality relationship between sectoral stock markets (agricultural, financial, industrial, and mining sectors) and economic growth in the short and long term as well as to analyze whether it has similar types or not. The data used is quarterly time-series data (first quarter 2009 to fourth 2019). To determine the causality relationship, this study conducts a variable and multivariate causality test. The results of the varying granger causality test show that there is only a one-way relationship, where the economic growth of the agriculture sector affects its shares. A one-way relationship also occurs in stocks of the industrial sector, which has an influence on economic growth. The multivariate causality test shows that the economic growth of the agricultural sector has a two-way causality relationship, and it also exists between the industrial sector and the financial sector stock markets. The two-way causality relationship between the stock market and sectoral economic growth is a convergence towards long-term equilibrium. The findings of this study suggest that the government through the Financial Services Authority and the Indonesia Stock Exchange have to maintain stability in the stock market as a supporter of the national economy.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.731-740
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• 2021

This research aims to investigate the determinants of real effective exchange rate in emerging ASEAN countries, including Indonesia, Malaysia, Philippines, Thailand, and Vietnam. The research was conducted by using quarterly time series data set from 1980Q1 to 2020Q3. Cointegration and the error correction model (ECM) methods were applied to test the long run and short run relationship of the real effective exchange rate and its determinants. The results indicate that the ratio of foreign direct investment to GDP and the government spending have significantly positive impact on real effective exchange rate in the Emerging ASEAN countries. The trade opening had influencing real effective exchange rate in most the Emerging ASEAN countries, except Vietnam. In addition, the international reserve (INR) had significant long-run impacts variables on real effective exchange rate in Malaysia, Thailand and Vietnam. In the short run equilibrium, the error collection term suggest that Indonesia and Malaysia are the fastest speed adjustment to equilibrium. In addition, the term of trade influence the real effective exchange rate in Indonesia, Malaysia, and the Philippines but it is not in Thailand and Vietnam. However, FDI is a major factor of the real effective exchange rate in Vietnam, but not for other countries.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• Asian Journal of Business Environment
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• v.14 no.3
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• pp.23-35
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• 2024

Purpose: The study focuses on the impact of Foreign Direct Investment (FDI), trade openness, and energy consumption on carbon dioxide emissions in the Ivory Coast. It aims to quantitatively evaluate the effects of FDI, energy consumption, and trade openness on CO₂ emissions in Ivory Coast. Research design, data, and methodology: The research uses an econometric framework and the Autoregressive Distributed Lag (ARDL) model to analyze time-series data from 1980 to 2021 between these factors. Results: The analysis revealed that FDI significantly impacts the carbon dioxide emissions, FDI showed a negative impact on carbon emissions in the long-run equilibrium term. Also, energy consumption impacted CO₂ emissions in the long-run equilibrium term. Conclusion: To mitigate the upsurge of CO₂ emissions in the Ivorian context, concrete policy, including enactment and adherence to strict environmental regulations, adoption and prioritization of eco-friendly products and technologies, and investment in renewable energy infrastructure are recommended. The study contributes to the global discussion on sustainable development by offering a model for similar assessments in other emerging nations facing simultaneous economic growth and environmental conservation challenges.

• Asia-Pacific Journal of Business
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• v.7 no.1
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• pp.1-10
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• 2016

This study attempts to investigate the effects of different types of debts on economic growth in Bangladesh using time series data spanning from 2000 to 2015. In this study, the RDL model has been applied to determine the long run relationship among the selected variables. The result of the ARDL model shows that there exists a long term relationship between economic growth and the debt variables. It was evident from the findings that there exists bidirectional causality between public sector external debt and economic growth. Causality between private external debt and economic growth has been found to be insignificant. However, causality between domestic debt and economic growth showed a unidirectional causality from domestic debt to economic growth and not vice versa. Causality tests suggest that impact of domestic debt on economic growth is more effective compared to external debts.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.573-587
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• 2019

A multi-temporal approach using remotely sensed time series data obtained over multiple years is a very useful method for monitoring land covers and land-cover changes. While spectral-based methods at any particular time limits the application utility due to instability of the quality of data obtained at that time, the approach based on the temporal profile can produce more accurate results since data is analyzed from a long-term perspective rather than on one point in time. In this study, a multi-temporal approach applying a multi-periodic harmonic model is proposed for classification of remotely sensed data. A harmonic model characterizes the seasonal variation of a time series by four parameters: average level, frequency, phase, and amplitude. The availability of high-quality data is very important for multi-temporal analysis.An satellite image usually have many unobserved data and bad-quality data due to the influence of observation environment and sensing system, which impede the analysis and might possibly produce inaccurate results. Harmonic analysis is also very useful for real-time data reconstruction. Multi-periodic harmonic model is applied to the reconstructed data to classify land covers and monitor land-cover change by tracking the temporal profiles. The proposed method is tested with the MODIS and GOCI NDVI time series over the Korean Peninsula for 5 years from 2012 to 2016. The results show that the multi-periodic harmonic model has a great potential for classification of land-cover types and monitoring of land-cover changes through characterizing annual temporal dynamics.

• Journal of Agricultural Extension & Community Development
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• v.24 no.1
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• pp.63-81
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• 2017

The purpose of this study is to analyze the changes of time series, the use by farm type and the causes of farm household debt. First, the mid and long term changes in farm household debt over the past 50 years have increased. Since 2010, the share of non-agricultural debt has exceeded the share of agricultural debt. Second, as a result of the analysis of the farm household debt use by farm type - full time&part time, farming type, land size, age, family members - there was a difference between the agricultural and the non-agricultural debt according to the type of farm household in a significant level of 1%. Finally, as a result of the cause analysis of the farm household debt, the related non-agricultural expenditure variables and the dummy variable of the manager's age, family member and land size has a common influence on the farm household debt increase.