A Study On The Relationship Between The Size Of Electronic Market And The Total Retail Sales Of Social Consumer Goods In China

With the rapid development of information and network technology, electronic commerce has become the hot spot and trend of economic. In our country, Information network communication technology is becoming more and more mature. After nearly twenty years of development, Electronic commerce has become a power that can’t be ignored in promoting the social economic development in our country. At the same time it also profoundly affects people’s economic life mode and habit. The relationship between e-commerce and domestic economic is closely. The leading role in promoting our country’s economic is becoming stronger and stronger. According to relevant data, In 2014 the network retail market transactional scale in China has expanded to 2863700000000 yuan, up 51.9% over the previous year. Accounts for more than 1/10 in the social total retail sales. Through studying the change of total retail sales, we can effective analyze the change of domestic retail market situation. So, it can provide the necessary reference to help we know the degree of economic boom at some stage. Therefore, after quantitative analysising of the relationship between China’s e-commerce and total retail sales of society, we can explore the the relationship between China’s e-commerce and retail industry. So the research has great significance for us. What’s more, based on it we are able to provide scientific and effective data reference to our government agency to help them make some policies developing country’s economic. In this paper we classify e-commerce as medium enterprises B2 B, scale enterprises B2 B, online shopping and online travel. Then explore the relation between each of them and the total retail sales of social consumer goods in China.In this paper, we use Eviews 7 software for data analysis.We select the quarterly data of the first quarter of 2002 to the fourth quarter of 2014 in our country e-commerce to explore the relations between time series. We introduce the correlation analysis to explore the correlate relationship between the variables. Use the sequences have a ciontegion test to know whether there is a long-term stable equilibrium relationship between them. Then establish an error correction model in order to explore the short-term fluctuations between them. Based on the constructed error correction model we also bring dummy variable to construct asymmetric error correction model for analysis. The purpose is to explore what different effects will be bring on its next stage when the present explanatory variables greater than the equilibrium values and smaller than the equilibrium values. Introduce Grainger causality test between the selected target variables to explore the causal relationship. At last we introduce the other prediction methods like smoothing and ARIMA model to forecast relevant data of social total retail sales of consumer goods. Then compare the predicted result of error correction model and them to judge the effect of error correction model for forecasting feasibility in practical applications.The research results show that there is a strong correlation between each segment market of e-commerce and the total retail sales of social consumer goods in China. After calculating the correlation coefficient we know that there is a strong correlation between each segment market of e-commerce and the total retail sales of social consumer goods in China. And there is a long-term stable equilibrium relationship between the target sequences, so we can construct the regression equations, get the quantitative long-term equilibrium relation between them. Through the construction of non symmetric error correction model we know when the present explanatory variables greater than the equilibrium values and smaller than the equilibrium values. the impact is not great to next period. After Grainger causality testing, we know it has the Granger causality relationship between social total retail sales of consumer goods(SH) and medium-sized enterprise transaction volume(ZB) in the case of 3 order lag. So is the relationship between Social total retail sales of consumer goods(SH) and the network shopping transactions(WG). But it has no Granger causality relationship between social total retail sales of consumer goods(SH) and online travel trades(ZX), so is the relationship between social total retail sales of consumer goods(SH) and log of scale enterprises B2B(LNGB). In prediction, the result indicates compared with smoothing and ARIMA model, the prediction accuracy of error correction model is slightly less than them. But the prediction accuracy is very little. It’s only 1.29%. The effect is good. The predicted result in our acceptable range.