Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment
Bibliographic Data
| ID | 23508950 |
|---|---|
| Authors | Thomas Pave Sohnesen (0000-0003-3942-8568), Niels Stender |
| Year | 2016 |
| Publication date | 2016-03-01 |
| Open Access | No |
| Type | BOOK |
| Publisher | World Bank, Washington, DC (PUBLISHER) |
| DOI | 10.1596/1813-9450-7612 |
| OpenAlex | W2769217321 |
| Language | EN |
| Citations received | 1 |
| References cited | 10 |
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.
Econometrics · Economic growth · Economics · Poverty · Random forest · Statistics · Artificial Intelligence · Artificial Intelligence in Healthcare · Computer Science · Income, Poverty, and Inequality · Mathematics · Statistical Methods and Inference
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| Citations per year | 0.5 |
|---|---|
| Citation span | 2024 - 2024 (1) |
| Highly cited | No |