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SAE: Stata module to provide commands and mata functions devoted to unit level small area estimation

Description

sae is a Stata family of functions for small area estimation, using the methodology from Elbers, Lanjouw, and Lanjouw (2003). The package introduces new mata functions and a plugin used to circumvent memory limitations that inevitably arise when working with larger datasets.

Suggested Citation

Minh Nguyen & Paul Corral & Joao Pedro Azevedo & Qinghua Zhao, 2018. "SAE: Stata module to provide commands and mata functions devoted to unit level small area estimation," Statistical Software Components S458525, Boston College Department of Economics, revised 10 Sep 2018.

or

Nguyen,Minh Cong & Corral Rodas,Paul Andres & Azevedo,Joao Pedro Wagner De & Zhao,Qinghua, 2018. "sae : A Stata Package for Unit Level Small Area Estimation," Policy Research Working Paper Series 8630, The World Bank.

References

Bedi, T., A. Coudouel, and K. Simler (2007). More than a pretty picture: using poverty maps to design better policies and interventions. World Bank Publications.

Demombynes, G., C. Elbers, J. O. Lanjouw, and P. Lanjouw (2008). How good is a map? putting small area estimation to the test. Rivista Internazionale di Scienze Sociali, 465–494.

Elbers, C., J. O. Lanjouw, and P. Lanjouw (2002). Micro-level estimation of welfare. 2911.

Elbers, C., J. O. Lanjouw, and P. Lanjouw (2003). Micro–level estimation of poverty and inequality. Econometrica 71 (1), 355–364.

Foster, J., J. Greer, and E. Thorbecke (1984). A class of decomposable poverty measures. Econometrica: Journal of the Econometric Society, 761–766.

Harvey, A. C. (1976). Estimating regression models with multiplicative heteroscedasticity. Econometrica: Journal of the Econometric Society, 461–465.

Haslett, S., M. Isidro, and G. Jones (2010). Comparison of survey regression techniques in the context of small area estimation of poverty. Survey Methodology 36 (2), 157–170.

Henderson, C. R. (1953). Estimation of variance and covariance components. Biometrics 9 (2), 226–252.

Huang, R. and M. Hidiroglou (2003). Design consistent estimators for a mixed linear model on survey data.

Proceedings of the Survey Research Methods Section, American Statistical Association (2003), 1897–1904.

Molina, I. and J. Rao (2010). Small area estimation of poverty indicators. Canadian Journal of Statistics 38 (3), 369–385.

Rao, J. N. and I. Molina (2015). Small area estimation. John Wiley & Sons.

Tarozzi, A. and A. Deaton (2009). Using census and survey data to estimate poverty and inequality for small areas. The review of economics and statistics 91 (4), 773–792.

Van der Weide, R. (2014). GLS estimation and empirical bayes prediction for linear mixed models with heteroskedasticity and sampling weights: a background study for the povmap project. World Bank Policy Research Working Paper (7028).

Zhao, Q. (2006). User manual for povmap. World Bank. (link)

Handle: RePEc:boc:bocode:s458525

Note:

This module should be installed from within Stata by typing "ssc install sae". Windows users should not attempt to download these files with a web browser.

Keywords

sae; poverty; ELL; gls; poverty map; small area estimation;

Authors:

Minh Cong Nguyen
mnguyen3@worldbank.org
World Bank

Paul Andres Corral Rodas
pcorralrodas@worldbank.org
World Bank

João Pedro Azevedo
jazevedo@worldbank.org
World Bank
personal page

Qinghua Zhao
World Bank

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SAE: Stata module to provide commands and mata functions devoted to unit level small area estimation

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