Recent studies harnessing geospatial big data and machine learning have significantly advanced poverty mapping … machine learning-based poverty mapping, testing whether spatial regression and machine learning techniques produce more … learning-based poverty mapping. …

new dataset of consistent inequality series,allowing us to explore problems of measurement error. In addition, the new … data allowus to perform parametric non-1inear estimation of Lorenz curves from grouped data.This in turn al1ows us to … estimate the entire income distribution; computing alternativeinequality indexes and poverty estimates. Finally, we have used …

Geographically weighted small area methods have been studied in literature for small area estimation. Although these … approaches are useful for the estimation of small area means efficiently under strict parametric assumptions, they can be very … spatial non-stationarity. Mean squared error estimation is performed by two different analytic approaches that account for the …

This research article seeks to specify and measure the determinants of multidimensional poverty in Colombia using the … poverty between neighbouring municipalities, the existence of clusters and hot spots in the Pacific Chocó region, the …

This paper considers the problem of identification, estimation and inference in the case of spatial panel data models … errors. A quasi maximum likelihood (QML) estimation procedure is developed and the conditions for identification of spatial …