Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/213811 
Autor:innen: 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
GLO Discussion Paper No. 468
Verlag: 
Global Labor Organization (GLO), Essen
Zusammenfassung: 
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.
Schlagwörter: 
Welfare Modelling
Income Distributions
Poverty Predictions
Imputations
JEL: 
D31
D63
E64
O15
Dokumentart: 
Working Paper

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