Research on Information Applied Technology with Data Analysis of Flowers Auction Data Based on Cointegration and Granger Casualty

Article Preview

Abstract:

According to the research of the variables (supply quantity, volume of trade, failed auction rate and price) using the information applied technology with data, from Kunming flowers auction market, with cointegration and Granger casualty, the result shows that the price has an impact on supply quantity and volume of trade with one lagged period, the supply quantity and volume of trade affect the price with three lagged periods. At the same time, the price affects the failed auction rate with three lagged periods. The failed auction rate affects the price in the same way. With the analysis of above, the supply quantity and volume of trade may exit multicollinearity. It is proved by vector autoregression (VAR) model. The results will be a guide to the flowers auction.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

546-551

Citation:

Online since:

January 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Li-jun FAN, Hai-ru YANG. (2006) Research of auction applies on agricultural wholesale market in our country. Market modernization, 481(10): 44-45 (in Chinese).

Google Scholar

[2] Wen-chang WANG, Xi-hui LI, Jun-feng LI. (2001) The western characteristic economy development. Minorities press, Peking. (in Chinese ).

Google Scholar

[3] Li-li SUN, Hong GE, Yu-qiang FENG (2010) The research of online auction transaction price influence factors with real evidence. China Journal of information system, 4(1): 34-42.

Google Scholar

[4] Li-mei CUI, Yong LONG. (2011) Study on fluctuation of flowers auction between price and supply based on cobweb model. Journal of Anhui agricultural sciences, 39(22): 13724-13730, 13741 (In Chinese).

Google Scholar

[5] Bin DENG (2010) Research of fresh agricultural products auction price influence factors: KIFA flower auction as an example. Kunming. (in Chinese).

Google Scholar

[6] Vickrey W. (1967) Counter speculation auctions and competitive sealed tenders, Journal of Finance, 16: 8-37.

Google Scholar

[7] Zhi-yong HAN, Yi-ming WEI, Jian-lin JIAO, et al. (2004) On the cointegration and causality between Chinese GDP and energy consumption. System Engineering, 22(12): 18-20 (in Chinese).

Google Scholar

[8] Bo-qiang LIN. (2003) Electricity consumption and economic growth in China: based on production function. Management World, 11: 18-27 (in Chinese).

Google Scholar

[9] Shou-xiang XIE, Qing-hua TAN, Yang SONG. (2006) Influence factors of coal price correlation analysis and testing. Statistics and Decision, 22: 57-60 (in Chinses).

Google Scholar

[10] Zai-bin YANG, Xia KUANG. (2005) Co-integration study of relations between capital market and China's economic growth. Journal of Tongji university (natural science), 33(6): 848- 852 (in Chinese).

Google Scholar

[11] Drama B. G. H., Bouphanuvong C., Shen Y. (2011).

Google Scholar

[12] Ke-shan WANG, Jian-bin YU. (2008) Soybean futures price and spot price fluctuation relationship analysis in China. Lanzhou Academic Journal, 11: 81-83 (in Chinese).

Google Scholar

[13] Guang-qiang LUO, Kang-kang XIE. (2009) Research of supply and demand factors of pig price fluctuation in Hunan. Prices Monthly, 391(12): 29-32 (in Chinese).

Google Scholar