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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.

Linear Regression Models. --- Machine Learning. --- Macroeconomics and Economic Growth. --- Poverty Monitoring and Analysis. --- Poverty Reduction. --- Poverty. --- Prediction Methods. --- Random Forest. --- Rural Poverty Reduction. --- Science and Technology Development. --- Statistical and Mathematical Sciences. --- Tracking Poverty.