GLS Estimation and Empirical Bayes Prediction for Linear Mixed Models with Heteroskedasticity and Sampling Weights: A Background Study for the Povmap Project

14 Pages Posted: 20 Apr 2016

See all articles by Roy van der Weide

Roy van der Weide

World Bank; World Bank - Development Research Group (DECRG)

Date Written: September 1, 2014

Abstract

This note adapts results by Huang and Hidiroglou (2003) on Generalized Least Squares estimation and Empirical Bayes prediction for linear mixed models with sampling weights. The objective is to incorporate these results into the poverty mapping approach put forward by Elbers et al. (2003). The estimators presented here have been implemented in version 2.5 of POVMAP, the custom-made poverty mapping software developed by the World Bank.

Keywords: Inequality

Suggested Citation

van der Weide, Roy, GLS Estimation and Empirical Bayes Prediction for Linear Mixed Models with Heteroskedasticity and Sampling Weights: A Background Study for the Povmap Project (September 1, 2014). World Bank Policy Research Working Paper No. 7028. Available at SSRN: https://ssrn.com/abstract=2495175

Roy Van der Weide (Contact Author)

World Bank ( email )

1818 H Street, N.W.
Washington, DC 20433
United States

World Bank - Development Research Group (DECRG)

1818 H. Street, N.W.
MSN3-311
Washington, DC 20433
United States

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