Pull Your Small Area Estimates Up by the Bootstraps

This paper presents a methodological update to the World Bank's toolkit for small area estimation. The paper reviews the computational procedures of the current methods used by the institution: the traditional ELL approach and the Empirical Best (EB) addition introduced to imitate the original...

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Main Authors:	Corral, Paul, Molina, Isabel, Nguyen, Minh
Format:	Journal Article
Published:	Taylor and Francis 2022
Subjects:	SMALL AREA ESTIMATION POVERTY MAPPING PARAMETRIC BOOTSTRAP EMPIRICAL BEST ELL APPROACH SIMULATION
Online Access:	http://hdl.handle.net/10986/36821

id	okr-10986-36821
recordtype	oai_dc
spelling	okr-10986-368212022-01-28T16:12:07Z Pull Your Small Area Estimates Up by the Bootstraps Corral, Paul Molina, Isabel Nguyen, Minh SMALL AREA ESTIMATION POVERTY MAPPING PARAMETRIC BOOTSTRAP EMPIRICAL BEST ELL APPROACH SIMULATION This paper presents a methodological update to the World Bank's toolkit for small area estimation. The paper reviews the computational procedures of the current methods used by the institution: the traditional ELL approach and the Empirical Best (EB) addition introduced to imitate the original EB procedure of Molina and Rao [Small area estimation of poverty indicators. Canadian J Stat. 2010;38(3):369–385], including heteroskedasticity and survey weights, but using a different bootstrap approach, here referred to as clustered bootstrap. Simulation experiments provide empirical evidence of the shortcomings of the clustered bootstrap approach, which yields biased and noisier point estimates. The document presents an update to the World Bank’s EB implementation by considering the original EB procedures for point and noise estimation, extended for complex designs and heteroscedasticity. Simulation experiments illustrate that the revised methods yield considerably less biased and more efficient estimators than those obtained from the clustered bootstrap approach. 2022-01-14T19:55:47Z 2022-01-14T19:55:47Z 2021-05-08 Journal Article Journal of Statistical Computation and Simulation 0094-9655 http://hdl.handle.net/10986/36821 CC BY-NC-ND 3.0 IGO http://creativecommons.org/licenses/by-nc-nd/3.0/igo World Bank Taylor and Francis Publications & Research Publications & Research :: Journal Article
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
topic	SMALL AREA ESTIMATION POVERTY MAPPING PARAMETRIC BOOTSTRAP EMPIRICAL BEST ELL APPROACH SIMULATION
spellingShingle	SMALL AREA ESTIMATION POVERTY MAPPING PARAMETRIC BOOTSTRAP EMPIRICAL BEST ELL APPROACH SIMULATION Corral, Paul Molina, Isabel Nguyen, Minh Pull Your Small Area Estimates Up by the Bootstraps
description	This paper presents a methodological update to the World Bank's toolkit for small area estimation. The paper reviews the computational procedures of the current methods used by the institution: the traditional ELL approach and the Empirical Best (EB) addition introduced to imitate the original EB procedure of Molina and Rao [Small area estimation of poverty indicators. Canadian J Stat. 2010;38(3):369–385], including heteroskedasticity and survey weights, but using a different bootstrap approach, here referred to as clustered bootstrap. Simulation experiments provide empirical evidence of the shortcomings of the clustered bootstrap approach, which yields biased and noisier point estimates. The document presents an update to the World Bank’s EB implementation by considering the original EB procedures for point and noise estimation, extended for complex designs and heteroscedasticity. Simulation experiments illustrate that the revised methods yield considerably less biased and more efficient estimators than those obtained from the clustered bootstrap approach.
format	Journal Article
author	Corral, Paul Molina, Isabel Nguyen, Minh
author_facet	Corral, Paul Molina, Isabel Nguyen, Minh
author_sort	Corral, Paul
title	Pull Your Small Area Estimates Up by the Bootstraps
title_short	Pull Your Small Area Estimates Up by the Bootstraps
title_full	Pull Your Small Area Estimates Up by the Bootstraps
title_fullStr	Pull Your Small Area Estimates Up by the Bootstraps
title_full_unstemmed	Pull Your Small Area Estimates Up by the Bootstraps
title_sort	pull your small area estimates up by the bootstraps
publisher	Taylor and Francis
publishDate	2022
url	http://hdl.handle.net/10986/36821
_version_	1764485963511234560

Pull Your Small Area Estimates Up by the Bootstraps

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