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...
| Main Authors: | , , |
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| Language: | English |
| Published: |
World Bank, Washington, DC
2020
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/714341590090749405/Pull-Your-Small-Area-Estimates-up-by-the-Bootstraps http://hdl.handle.net/10986/33819 |
| Summary: | 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. |
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