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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INFORMATION
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2016/03/01
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Document de travail (série numérotée)
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125655
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1
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1
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2018/04/24
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Disclosed
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Is random forest a superior methodology for predicting poverty? : an empirical assessment
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distributional impact of taxes
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