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This paper explores whether increasing market competition in China under its current market system enables improving Chinese bank efficiency levels similar to those in capitalist countries. Spatial inequality is considered in testing of bank efficiency. The empirical findings show that the Chinese government had to continually reform the banking industry in China to deal with foreign bank competition after China joined the WTO in 2001. Consequently, the cost and profit efficiency of the Chinese banking industry have progressed considerably. The extent of liberalization in a region enables cost reductions in finding foreign bank customers, thereby improving bank cost efficiency. The potential foreign customer sources generated by foreign investments and government expenditure in neighboring regions improve cost efficiency. The economic activities also generated by government fiscal expenditures improve bank profit efficiency. We further find that excessive competition caused by financial industry agglomeration generates a market crowding effect, reducing both cost efficiency and profit efficiency.

Keywords: regional economic factors, government policies, cost and profit efficiency, China’s banking industry, spatial econometric model.

JEL Classification Nos: G210, O160, P340, R120

The paper relates education expenditure, health expenditure and GDP per capital of SAARC bloc with its human development index during 19902016 in panel data analysis. The growth rates of human development indices and its structural breaks were analysed through semi log linear model and Bai Perron model. Pedroni, Kao and Johansen models of co integration were applied for long run association. Long run causality was verified by co integrating equations and short run causality was tested by the Wald test. The paper concludes that HDI of SAARC have been increasing with upward structural breaks. HDI is negatively related with education and health expenditures and positively related with GDP per capital during 19902016. They have at least one co integrating equation and there were significant long run causalities from  education expenditure, health expenditure and GDP per capita to the human development index of SAARC but they had no short run causalities. Rather, there was short causality from human development index to the health expenditure of the SAARC nations.

Key words: Human Development Index, education expenditure, health expenditure, random effect model, panel cointegration, panel vector error correction, short run causality, long run causality.

JEL classification: C22,C23,E24,F15,H15,I10,I18,J24,J64,O15,O40

Applying an extended ISLM model, this study finds that fiscal expansion reduced output and caused real appreciation and that monetary expansion increased output and caused real depreciation. Therefore, except for the negative impact of fiscal expansion on output, the Mundell Fleming model applies to India.

Keywords: fiscal expansion, monetary expansion, exchange rates, Mundell Fleming model.

JEL Codes: E52, E62, F41.

The argument that higher stock prices would presage faster economic growth makes a lot of sense. Since stock prices are the present discounted value of the future stream of expected dividends, an increase in anticipated economic activity — and hence earnings and dividends — should be associated with a boost in the stock market. Growth of economy is represented by GDP and stock market return is represented by NSE Nifty. In order to examine the relationship between stock return and GDP, time series econometric tools such as unit root test Augmented Dickey Fuller Test (ADF) is employed to test whether data is stationary or not. Further to examine the casual relation between stock return and GDP during entire sample period (Dec 2005 to Dec 2016) Cross correlation is employed. Any kind of association not found in this research.

Key words: GDP, CNX Nifty, volatility, Indian stock market, unit root test, Managing Risk.

The paper studied the impacts of India’s export to the seven African trading blocs during 1995-2016 especially on GDP growth rate, FDI inflows, inflation rate, Real Effective Exchange Rate, import concentration index and openness of the blocs which directly or indirectly help to speed up the process of trade and financial integration of the African blocs taking data from UNCTAD through Bai Perron model (2003), Fixed effect panel regression model, the Hausman test (1978, Fisher (1932)Johansen (1991), Kao (1999) and Pedroni (1999) co integration models. Vector Error Correction and Wald test (1943) were applied to test causality. The empirical results showed that the growth rate of India’s export to seven African blocs namely, CEMAC, COMESA, EAC, ECCAS, SACU, SADC and WAEMU have been increasing at the rate of 0.130.19 per cent per annum during 1995-2017 which have significant upward structural breaks. The fixed effect panel regression assured that one per cent increase in GDP growth rate, FDI inflows, inflation rate, of African blocs led to 0.101 per cent , 0.1185 per cent, 0.1839 increase in India’s export to African bloc blocs but one per cent increase in openness ,REERand import concentration index in African blocs led to 3.586 per cent decrease, 1.15% decrease, 1.388 per cent decrease in Indian export to African blocs during 19952017. Panel co integration showed that there are at least five co integrating vectors among them. There are insignificant long run causalities from import concentration index and openness index of 7 African blocs to GDP growth rate and REER. There is short term causality from REER of the African blocs to Indian export to their blocs. And there are short term causalities [i] from import concentration of African blocs to GDP growth rate of African blocs, [ii] from openness of African blocs to inflation rate of African blocs, and [iii] from FDI inflows of African blocs to REER of African blocs respectively. This research may find out to formulate policies on macro variables how to accelerate trade and financial integration of African blocs with India.

Key words: African blocs, India’s exports, panel co integration, panel vector error correction, short run causality, long run causality,

JEL classification codes: C33, F14, F15, F40, P33

Model order determination is a common problem in time series modeling. Due to the randomness of the observed values and the variability of the parameters under different orders, the problem of order determination becomes complicated. The effective order determination must consider both the goodness of model fitting and the degree of model simplification. This paper mainly introduces several commonly used order criterion. By using Python software data simulation is used to analysis and compare the order accuracy of different criterion under different sample sizes. The simulation results show that (1) When the sample size is small, the accuracy of an improved AIC criterion is obviously better than AIC criterion and BIC criterion. (2) With the sample increasing, BIC criterion’s order accuracy converges to 1 gradually according to probability, while AIC criterion and new AIC criterion tend to fit high order model, which cannot give the consistent estimation of the real order of the model.

Keywords: time series; model order determination; Python simulation; order accuracy.