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 predictions. However, none of the methods consistently provides accurate predictions of poverty over time, highlighting that technical model fitting by any method within a single year is not always, by itself, sufficient for accurate predictions of poverty over time.
Details
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Author
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Document Date
2016/03/18
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Document Type
Policy Research Working Paper
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Report Number
WPS7612
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Volume No
1
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Total Volume(s)
1
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Country
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Region
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Disclosure Date
2016/03/18
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Disclosure Status
Disclosed
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Doc Name
Is random forest a superior methodology for predicting poverty ? an empirical assessment
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Keywords
small area estimation;development research group;labor force survey;impact of migration;impact on poverty;department of economics;loss function;rural area;simple average;consumption;selection method;estimation method;machine learning;consumption datum;total sample;confidence interval;regression equation;linear regression;development policy;pattern recognition;agricultural growth;occupational mobility;national poverty;spatial poverty;model prediction;poor household;public policy;data mining;open access;poverty targeting;model fitting;consumption proxy;consumption poverty;
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Citation
Pave Sohnesen,Thomas Stender,Niels
Is random forest a superior methodology for predicting poverty an empirical assessment (English). Policy Research working paper,no. WPS 7612 Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/777401467987858907/Is-random-forest-a-superior-methodology-for-predicting-poverty-an-empirical-assessment