techniques, in particular kernel density estimation, in estimating poverty from grouped data. Using Monte Carlo simulations, we … show that kernel density estimation gives rise to nontrivial biases in estimated poverty levels that depend on the … bandwidth, kernel, poverty indicator, size of the dataset, and data generating process. Furthermore, the empirical bias in the …

Kernel density estimation (KDE) has been prominently used to measure poverty from grouped data (Sala-i-Martin, 2006 … distributions and unit data from several household surveys, we compare KDE-based poverty estimates with their true and survey … counterparts. We find that the technique gives rise to biases in poverty estimates the sign and magnitude of which vary with the …

field of distributional analysis. To this end, it covers subjects including inequality, poverty, and the modeling of income …

of Economics and Finance -- Poverty, Inequality and Development Studies with Machine Learning -- Machine Learning for … learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise …, students, policy-makers, and practitioners, who are using econometrics in theory or in practice. . …

The eradication of poverty, which was the first of the millennium development goals (MDG) established by the United … estimates of poverty at the national level and larger international regions. However, when one starts to disaggregate data by … of poverty and inequality at the national level and for the 32 federate entities (31 states and Mexico City) with …

The eradication of poverty, which was the first of the millennium development goals (MDG) established by the United … estimates of poverty at the national level and larger international regions. However, when one starts to disaggregate data by … of poverty and inequality at the national level and for the 32 federate entities (31 states and Mexico City) with …