• The Journal of Asian Finance, Economics and Business
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• 제7권8호
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• pp.533-542
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• 2020

This study estimates the nature of the relationship of entrepreneurship and business confidence on youth unemployment in the Philippines over the 2001-2017 period. The paper employed a range of cointegrating regression models, namely, autoregressive distributed lag (ARDL) bounds testing approach, Johansen-Juselius (JJ) and Engle-Granger (EG) cointegration models, dynamic OLS, fully modified OLS, and canonical cointegrating regression (CCR) estimation techniques. The Granger causality based on error correction model (ECM) was also performed to determine the causal link of entrepreneurship and business confidence on youth unemployment. The ARDL bounds testing approach, Johansen-Juselius (JJ) and Engle-Granger (EG) cointegration models confirmed the existence of long-run equilibrium relationship of entrepreneurship and business confidence on youth unemployment. The long-run coefficients from JJ and dynamic OLS show significant long-run and positive relationship of entrepreneurship and business confidence on youth unemployment. While results of the long-run coefficients from fully modified OLS and canonical cointegrating regression (CCR) found that only entrepreneurship has significant and positive relationship with youth unemployment in the long-run. The Granger causality based on error correction model (ECM) estimates show evidence of long-run causal relationship of entrepreneurship and business confidence on youth unemployment. In the short-run, increases in entrepreneurship and business confidence causes youth unemployment to decrease.

• The Journal of Asian Finance, Economics and Business
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• 제5권2호
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• pp.15-24
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• 2018

The study examines the magnitude of economic spillover and the impact of foreign direct investment (FDI) inflows on the efficiency of the bank industry in China. This study employs unit root tests, cointegration tests and cointegrating regression analysis, including fully modified ordinary least squares (FMOLS), canonical cointegrating regression (CCR) and dynamic OLS (DOLS) to test the proposed hypotheses. The sample is restricted to the period of time in which monthly data is available and comparable among variables for the period from January 2002 to October 2013 (142 observations). All of the time series data was collected and retrieved from the People's Bank of China, China Monthly Statistics from the National Bureau of Statistics of China, and International Financial Statistics database from International Monetary Fund. The results of the Johansen cointegration test suggest that there is a long-run equilibrium relationship between FDI inflows, foreign exchange rate and banks performance in China. The results of cointegrating regression analysis using FMOLS, CCR and DOLS suggest that M2 supply and FDI inflows are significant at the 0.01 level. The results confirm that FDI inflows in the banking sector are positively related to the increase of banks productivity and performance and short-term loans in China. However, the results suggest that Chinese Yuan currency exchange rate to U.S. dollar is not significant in the banking and financial industry of China.

• Asian Journal of Business Environment
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• 제8권4호
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• pp.37-44
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• 2018

Purpose - The artificial intelligence industry plays an increasingly significant role in stimulating the development of United States of America's economy. On account of this background, this paper attempts to explore the impact of artificial intelligence industry on United States of America's macroeconomy. Research design, data, and methodology - This paper mainly focuses on the impact of artificial intelligence industry on GDP, employment, real income, import, export and foreign direct investment. Furthermore, the Phillips-Perron test and Canonical cointegrating regression will be employed to examine the impact of artificial intelligence industry on United States of America's macroeconomy with a sample form 2010-Q1 to 2017-Q4. Results - Via the empirical analysis, the results reveal that the artificial intelligence industry has a positive effect on United States of America's GDP, employment, real income, export and foreign direct investment. Conversely, the artificial intelligence industry has a negative effect on United States of America's import. Conclusions - In summary, the impact of artificial intelligence industry on United States of America's macroeconomy is positive and significant in statistics. Therefore, the government of United States of America should put more input into artificial intelligence industry.

• The Journal of Asian Finance, Economics and Business
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• 제5권3호
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• pp.31-41
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• 2018

The study examines economic and environmental impacts of mass tourism on regional tourism destinations, particularly the establishment of "Ten New Bali", in Indonesia. The sample is restricted to the period of time in which annual data is available and comparable among variables from 1980 to 2015 (36 observations). All of the time series data was collected and retrieved from the World Development Indicator database published by the World Bank. This study applies cointegrating regression analysis using the fully modified OLS, canonical cointegrating regression, and dynamic OLS. The results of the study suggest that 1) there is a long-run equilibrium relationship between tourism receipts, environmental degradation and economic growth in Indonesia, 2) tourism growth and agriculture land growth are positively related to an increase of total output in the short-run in Indonesia, and 3) arable land is significant at the 0.01 level, but forest rents and CO2 from transport are not significant in the short-run in Indonesia. The results confirm that arable land is negatively related to an increase of total output in Indonesia. That is, when tourism growth in the economy is getting realized it shows that the environmental degradation increases greatly in inverse in the model, eventually negative impacts to the environment.

• The Journal of Asian Finance, Economics and Business
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• 제6권2호
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• pp.63-73
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• 2019

The paper aims to investigate relationships between technology and innovation management, total factor productivity and economic growth in China. By comparing the trends in total factor productivity growth of industrialized economies (i.e. OECD), this study intends to showcase the importance of total factor productivity progress in the Chinese economy. The study employs time series data of an annual basis for the period from 1977 to 2016 retrieved from the World Development Indicator. The study employs unit root test, cointegration test, fully modified least squares estimation method, canonical cointegrating regression and dynamic least squares estimation method to test the hypotheses. The results of the cointegrating regression analysis show that manufacturing growth leads to an increase of total factor productivity in the short-run in China. The findings of the study suggest that manufacturing (i.e. technology and product innovation) is positively related to the increase of total factor productivity in the short-run and total output growth in the long-run. The findings suggest that promoting technology and innovation management and supporting R&D subsidies may reduce the marginal cost of conducting R&D and increase the rate of technology and innovation management and R&D activity and therefore, the total factor productivity growth rate.

• 자원ㆍ환경경제연구
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• 제25권2호
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• pp.141-178
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• 2016

본 논문은 실시간으로 측정되는 자동원격검침(AMR) 전력수요량을 사용하여 산업별 전력수요의 기온효과에 대한 특성과 패턴을 분석하였다. AMR 전력사용량의 시계열적 특징으로부터 장기 추세효과와 중기 기온효과 그리고 단기 특수일 효과로 구성되는 공적분 모형을 구축하고, 기온효과를 연속적인 기온반응함수를 통하여 분석하기 위하여 기온반응함수를 푸리에 플렉서블 폼(Fourier Flexible Form; FFF) 비선형 함수로 추정하였다. 추정 결과 도출된 기온반응함수와 기온효과를 통하여 기온효과가 뚜렷하게 나타나는 서비스업군과 기온효과가 미약하게 나타나는 제조업군으로 구분하였다. 그리고 기온효과가 뚜렷하게 나타나는 서비스업군을 기온반응함수의 추정치에 근거하여 여름피크 산업과 겨울피크 산업으로 구분하였다. 이러한 실증분석 결과는 산업별, 계절별 전력수요관리정책 수립에 정책적 기초를 제공한다. 또한 실시간으로 측정되는 AMR 전력수요량 분석이라는 점에서 시차의 발생없이 신속하게 전력수요관리에 반영될 수 있다는 의미가 있다.