EconPapers    
Economics at your fingertips  
 

Prediction and Analysis of Chinese Rural Households’ Consumption Level Based on the ARIMA Model

Jian-biao Yan and Qiang Li

Asian Agricultural Research, 2011, vol. 03, issue 03, 4

Abstract: By using the software SAS9.2 and the relevant data of consumption level of rural residents in China from 1952 to 2008, the ARIMA model is established. The model is used to analyze and forecast the time series of the consumption level of Chinese rural residents. The results show that in the near future period of time, the consumption level of Chinese rural residents will be further increased. In 2012, the level will break through per capital 5 000 yuan, almost 100 times more than that in the primary time period. But consumption level does not equal to living standard. To let farmers lead a good life, the government should follow the objective laws, take the overall situation into consideration; coordinate the relations among farmers’ consumption level, national subsidies and farmers’ production enthusiasm. Therefore, It suggested that the historical and objective factors should be attached more importance to,raising farmers’ income and allaying farmers’ fear were effective measures in developing the consumptive potential of rural market and promoting the economic sustainable development.

Keywords: Agribusiness (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/113428/files/3-19.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:asagre:113428

DOI: 10.22004/ag.econ.113428

Access Statistics for this article

More articles in Asian Agricultural Research from USA-China Science and Culture Media Corporation
Bibliographic data for series maintained by AgEcon Search ().

 
Page updated 2025-03-19
Handle: RePEc:ags:asagre:113428