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.

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随机森林是预测贫困的优越方法吗？实证评估

随机森林是许多研究领域中数据驱动预测的常用方法。在经济学和贫困预测中，很少使用随机森林。比较 6 个国家同年调查中的样本外预测结果表明，随机森林通常比当前的普遍做法（用逐步和套索选择的变量进行多重插补）更准确，表明这种方法有助于更好地预测贫困。然而，没有一种方法能够始终如一地提供对贫困的准确预测，这突出表明，在一年内通过任何方法拟合的技术模型本身并不总是足以准确预测贫困随着时间的推移。