In 2015,China and India's population represented approximately 35.74%of the total number of people living in the world.Due to the historical context and behavior of the most relevant indicators,this study proposes...In 2015,China and India's population represented approximately 35.74%of the total number of people living in the world.Due to the historical context and behavior of the most relevant indicators,this study proposes to utilize a wide variety of demographic,economic,and production indicators from 1952 to 2015 to assess their impact on the GNI in China and India.A comprehensive and new fangled modeling process with stepwise,regularization and distributed lag regression approaches is presented.Accordingly,theoretical results were corroborated through extensive diagnostic tests and an empirical check of the models'predictive capacity.The findings show that GNI in China is most influenced by variables such as reserves in foreign currency and the dependency ratio;whereas,variables of energy production and birth rate were generated for India.Therefore,it's the timing for China to relax the universal two-child policy.Due to the current value below the substitution rate,a gloomy outlook for China's future population and economy is predicted.Conversely,a positive outlook is forecasted for India,given the low price in the future of oil-India's primary raw material.展开更多
This paper explores the macroeconomic determinants of non-performing loans(NPL) in 19 Asian countries(low to high income economies) using the Generalized Method of Moments estimation approach based on the economic dat...This paper explores the macroeconomic determinants of non-performing loans(NPL) in 19 Asian countries(low to high income economies) using the Generalized Method of Moments estimation approach based on the economic data for the period between 1998 and 2015. The categorization of the economies is based on the average gross national income per capita as set by the World Bank.Specifically, the paper aims to evaluate if the determinants of NPL vary with the income levels of the countries. The results indicate that the NPL is strongly influenced by the inflation rate. The effect is,however, negative in the high-income and the middle-income countries and positive in the low-income countries. The GDP per capita has a dynamic negative relationship with the NPL in the high-income and the low-income countries. The remittance has a significant positive association in the high-income and a significant negative association in the low-income countries. Similarly, the unemployment rate has a positive effect on NPL in the middle-income and the low-income countries. With the rise in the official exchange rate, the NPL level increases in the low-income countries. The overall estimation results suggest that the NPL in Asian banking system depend on some key macroeconomic variables,such as unemployment rate, inflation rate, official exchange rate, remittance received and gross domestic product per capita, and these associations vary with the income level of the countries. Therefore,economic level of a country should be carefully considered while formulating credit policy to minimize credit risks in the banking system.展开更多
基金supported by the National Natural Science Foundation of China(no.71773012,12026239)Natural Science Foundation of Liaoning Province LN2020J35Research Project of Dongbei University of Finance and Economics(DUFE2020Y22).
文摘In 2015,China and India's population represented approximately 35.74%of the total number of people living in the world.Due to the historical context and behavior of the most relevant indicators,this study proposes to utilize a wide variety of demographic,economic,and production indicators from 1952 to 2015 to assess their impact on the GNI in China and India.A comprehensive and new fangled modeling process with stepwise,regularization and distributed lag regression approaches is presented.Accordingly,theoretical results were corroborated through extensive diagnostic tests and an empirical check of the models'predictive capacity.The findings show that GNI in China is most influenced by variables such as reserves in foreign currency and the dependency ratio;whereas,variables of energy production and birth rate were generated for India.Therefore,it's the timing for China to relax the universal two-child policy.Due to the current value below the substitution rate,a gloomy outlook for China's future population and economy is predicted.Conversely,a positive outlook is forecasted for India,given the low price in the future of oil-India's primary raw material.
基金Supported by the CAS-TWAS President’s Fellowship 2014 to the First Author from the Chinese Academy of Sciences,Beijing,China and the World Academy of Sciences,Trieste,Italy
文摘This paper explores the macroeconomic determinants of non-performing loans(NPL) in 19 Asian countries(low to high income economies) using the Generalized Method of Moments estimation approach based on the economic data for the period between 1998 and 2015. The categorization of the economies is based on the average gross national income per capita as set by the World Bank.Specifically, the paper aims to evaluate if the determinants of NPL vary with the income levels of the countries. The results indicate that the NPL is strongly influenced by the inflation rate. The effect is,however, negative in the high-income and the middle-income countries and positive in the low-income countries. The GDP per capita has a dynamic negative relationship with the NPL in the high-income and the low-income countries. The remittance has a significant positive association in the high-income and a significant negative association in the low-income countries. Similarly, the unemployment rate has a positive effect on NPL in the middle-income and the low-income countries. With the rise in the official exchange rate, the NPL level increases in the low-income countries. The overall estimation results suggest that the NPL in Asian banking system depend on some key macroeconomic variables,such as unemployment rate, inflation rate, official exchange rate, remittance received and gross domestic product per capita, and these associations vary with the income level of the countries. Therefore,economic level of a country should be carefully considered while formulating credit policy to minimize credit risks in the banking system.