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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

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  • van der Weide, Roy

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.

Suggested Citation

  • van der Weide, Roy, 2014. "GLS estimation and empirical bayes prediction for linear mixed models with Heteroskedasticity and sampling weights : a background study for the POVMAP project," Policy Research Working Paper Series 7028, The World Bank.
    • Handle: RePEc:wbk:wbrwps:7028
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    References listed on IDEAS

    as
    1. Elbers, Chris & Fujii, Tomoki & Lanjouw, Peter & Ozler, Berk & Yin, Wesley, 2007. "Poverty alleviation through geographic targeting: How much does disaggregation help?," Journal of Development Economics, Elsevier, vol. 83(1), pages 198-213, May.
    2. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    3. Elbers, Chris & van der Weide, Roy, 2014. "Estimation of normal mixtures in a nested error model with an application to small area estimation of poverty and inequality," Policy Research Working Paper Series 6962, The World Bank.
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    Cited by:

    1. Corral Rodas,Paul Andres & Molina,Isabel & Nguyen,Minh Cong, 2020. "Pull Your Small Area Estimates up by the Bootstraps," Policy Research Working Paper Series 9256, The World Bank.
    2. Newhouse David, 2020. "Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 45-50, August.
    3. Newhouse,David Locke & Merfeld,Joshua David & Ramakrishnan,Anusha Pudugramam & Swartz,Tom & Lahiri,Partha, 2022. "Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning," Policy Research Working Paper Series 10175, The World Bank.
    4. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    5. Paul Corral & Kristen Himelein & Kevin McGee & Isabel Molina, 2021. "A Map of the Poor or a Poor Map?," Mathematics, MDPI, vol. 9(21), pages 1-40, November.
    6. Isabel Molina, 2020. "Discussion of "Small area estimation: its evolution in five decades", by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 40-44, August.

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