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.