Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment

Thomas Sohnesen and Niels Stender

Poverty & Public Policy, 2017, vol. 9, issue 1, 118-133

Abstract: Random forest (RF) is in many fields of research a common method for data‐driven predictions. Within economics and prediction of poverty, RF is rarely used. Comparing out‐of‐sample predictions in surveys for the same year in six countries shows that RF 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.

Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://doi.org/10.1002/pop4.169

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:povpop:v:9:y:2017:i:1:p:118-133

Access Statistics for this article

More articles in Poverty & Public Policy from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().