Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/213811
Authors: 
Verme, Paolo
Year of Publication: 
2020
Series/Report no.: 
GLO Discussion Paper No. 468
Abstract: 
OLS models are the predominant choice for poverty predictions in a variety of contexts such as proxy-means tests, poverty mapping or cross-survey impu- tations. This paper compares the performance of econometric and machine learning models in predicting poverty using alternative objective functions and stochastic dominance analysis based on coverage curves. It finds that the choice of an optimal model largely depends on the distribution of incomes and the poverty line. Comparing the performance of different econometric and machine learning models is therefore an important step in the process of opti- mizing poverty predictions and targeting ratios.
Subjects: 
Welfare Modelling
Income Distributions
Poverty Predictions
Imputations
JEL: 
D31
D63
E64
O15
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.