Is random forest a superior methodology for predicting poverty? : an empirical assessment (English)

Random forest is in many fields of research a common method for data driven predictions. Within economics and prediction of poverty, random forest is rarely used. Comparing out-of-sample predictions in surveys for same year in six countries shows that random forest is often more accurate than current common practice (multiple imputations with variables selected by stepwise and Lasso), suggesting that this method could contribute to better poverty...

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Author

Pave Sohnesen,Thomas;

Stender,Niels;
Document Date
2016/03/01
Document Type
Working Paper (Numbered Series)
Report Number
125655
Volume No
1
Total Volume(s)
1
Country
World,
Region
Other,
Disclosure Date
2018/04/24
Disclosure Status
Disclosed
Doc Name
Is random forest a superior methodology for predicting poverty? : an empirical assessment
Keywords
distributional impact of taxes

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Pave Sohnesen,Thomas; Stender,Niels.

Is random forest a superior methodology for predicting poverty? : an empirical assessment (English). Poverty and Equity Global Practice Working Paper Series|no. 066 Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/343541524569863173

Is random forest a superior methodology for predicting poverty? : an empirical assessment (English)

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