Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China
AbstractChina is a major developing country where farmers account for over 57% of the population. Thus, promoting a rural economy is crucial if the Chinese government is to improve the quality of life of the nation as a whole. To frame scientific and effective rural policy or economic plans, it is useful and necessary for the government to predict the income of rural households. However, making such a prediction is challenging because rural households income is influenced by many factors, such as natural disasters. Based on the Grey Theory and the Differential Evolution (DE) algorithm, this study first developed a high-precision hybrid model, DE–GM(1,1) to forecast the per capita annual net income of rural households in China. By applying the DE algorithm to the optimization of the parameter λ, which was generally set equal to 0.5 in GM(1,1), we obtained more accurate forecasting results. Furthermore, the DE–Rolling–GM(1,1) was constructed by introducing the Rolling Mechanism. By analyzing the historical data of per capita annual net income of rural households in China from 1991 to 2008, we found that DE–Rolling–GM(1,1) can significantly improve the prediction precision when compared to traditional models.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Omega.
Volume (Year): 40 (2012)
Issue (Month): 5 ()
Contact details of provider:
Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.