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

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 app...

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Bibliographic Details
Main Author: van der Weide, Roy
Language:English
en_US
Published: World Bank Group, Washington, DC 2014
Subjects:
CAPITA CONSUMPTION
DEVELOPMENT RESEARCH
ESTIMATORS
GEOGRAPHIC TARGETING
HOUSEHOLD INCOME
INCOME
INDEPENDENT VARIABLES
MATRICES
MATRIX
POVERTY ALLEVIATION
POVERTY INDICATORS
PREDICTION
PROBABILITIES
PROBABILITY
RA
RESEARCH METHODS
RESEARCH WORKING PAPERS
SAMPLE SIZE
STANDARD ERRORS
STATA
SURVEY DATA
TARGETING
YIELDS
Online Access:http://documents.worldbank.org/curated/en/2014/09/20197348/gls-estimation-empirical-bayes-prediction-linear-mixed-models-heteroskedasticity-sampling-weights-background-study-povmap-project
http://hdl.handle.net/10986/20332
Description
Summary: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.

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