Pull Your Small Area Estimates Up by the Bootstraps
After almost two decades of poverty maps produced by the World Bank and multiple advances in the literature, this paper presents a methodological update to the World Bank's toolkit for small area estimation. The paper reviews the computational...
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World Bank, Washington, DC
2020
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| Online Access: | http://documents.worldbank.org/curated/en/714341590090749405/Pull-Your-Small-Area-Estimates-up-by-the-Bootstraps http://hdl.handle.net/10986/33819 |
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okr-10986-338192022-09-20T00:12:01Z Pull Your Small Area Estimates Up by the Bootstraps Corral, Paul Molina, Isabel Nguyen, Minh POVERTY MAPPING SMALL AREA ESTIMATE ELL EMPIRICAL BEST PARAMETRIC BOOTSTRAP ELBERS, LANJOUW AND LANJOUW HETEROSCEDASTICITY SURVEY WEIGHTS MEAN SQUARED ERROR ESTIMATION MONTE CARLO SIMULATION After almost two decades of poverty maps produced by the World Bank and multiple advances in the literature, 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 World Bank: the traditional approach by Elbers, Lanjouw and Lanjouw (2003) and the Empirical Best/Bayes (EB) addition introduced by Van der Weide (2014). The addition extends the EB procedure of Molina and Rao (2010) by considering heteroscedasticity and includes survey weights, but uses a different bootstrap approach, here referred to as clustered bootstrap. Simulation experiments comparing these methods to the original EB approach of Molina and Rao (2010) provide empirical evidence of the shortcomings of the clustered bootstrap approach, which yields biased point estimates. The main contributions of this paper are then two: 1) to adapt the original Monte Carlo simulation procedure of Molina and Rao (2010) for the approximation of the extended EB estimators that include heteroscedasticity and survey weights as in Van der Weide (2014); and 2) to adapt the parametric bootstrap approach for mean squared error (MSE) estimation considered by Molina and Rao (2010), and proposed originally by González-Manteiga et al. (2008), to these extended EB estimators. Simulation experiments illustrate that the revised Monte Carlo simulation method yields estimators that are considerably less biased and more efficient in terms of MSE than those obtained from the clustered bootstrap approach, and that the parametric bootstrap MSE estimators are in line with the true MSEs under realistic scenarios. 2020-05-28T15:42:40Z 2020-05-28T15:42:40Z 2020-05 Working Paper http://documents.worldbank.org/curated/en/714341590090749405/Pull-Your-Small-Area-Estimates-up-by-the-Bootstraps http://hdl.handle.net/10986/33819 English Policy Research Working Paper;No. 9256 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper |
| repository_type |
Digital Repository |
| institution_category |
Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
English |
| topic |
POVERTY MAPPING SMALL AREA ESTIMATE ELL EMPIRICAL BEST PARAMETRIC BOOTSTRAP ELBERS, LANJOUW AND LANJOUW HETEROSCEDASTICITY SURVEY WEIGHTS MEAN SQUARED ERROR ESTIMATION MONTE CARLO SIMULATION |
| spellingShingle |
POVERTY MAPPING SMALL AREA ESTIMATE ELL EMPIRICAL BEST PARAMETRIC BOOTSTRAP ELBERS, LANJOUW AND LANJOUW HETEROSCEDASTICITY SURVEY WEIGHTS MEAN SQUARED ERROR ESTIMATION MONTE CARLO SIMULATION Corral, Paul Molina, Isabel Nguyen, Minh Pull Your Small Area Estimates Up by the Bootstraps |
| relation |
Policy Research Working Paper;No. 9256 |
| description |
After almost two decades of poverty maps
produced by the World Bank and multiple advances in the
literature, 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 World Bank: the traditional approach by
Elbers, Lanjouw and Lanjouw (2003) and the Empirical
Best/Bayes (EB) addition introduced by Van der Weide (2014).
The addition extends the EB procedure of Molina and Rao
(2010) by considering heteroscedasticity and includes survey
weights, but uses a different bootstrap approach, here
referred to as clustered bootstrap. Simulation experiments
comparing these methods to the original EB approach of
Molina and Rao (2010) provide empirical evidence of the
shortcomings of the clustered bootstrap approach, which
yields biased point estimates. The main contributions of
this paper are then two: 1) to adapt the original Monte
Carlo simulation procedure of Molina and Rao (2010) for the
approximation of the extended EB estimators that include
heteroscedasticity and survey weights as in Van der Weide
(2014); and 2) to adapt the parametric bootstrap approach
for mean squared error (MSE) estimation considered by Molina
and Rao (2010), and proposed originally by González-Manteiga
et al. (2008), to these extended EB estimators. Simulation
experiments illustrate that the revised Monte Carlo
simulation method yields estimators that are considerably
less biased and more efficient in terms of MSE than those
obtained from the clustered bootstrap approach, and that the
parametric bootstrap MSE estimators are in line with the
true MSEs under realistic scenarios. |
| format |
Working Paper |
| 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 |
World Bank, Washington, DC |
| publishDate |
2020 |
| url |
http://documents.worldbank.org/curated/en/714341590090749405/Pull-Your-Small-Area-Estimates-up-by-the-Bootstraps http://hdl.handle.net/10986/33819 |
| _version_ |
1764479584297811968 |