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The fayherriot command for estimating small-area indicators

Author

Listed:
  • Christoph Halbmeier

    (Freie Universität Berlin)

  • Ann-Kristin Kreutzmann

    (Freie Universität Berlin)

  • Timo Schmid

    (Freie Universität Berlin)

  • Carsten Schröder

    (Freie Universität Berlin)

Abstract

We introduce a command, fayherriot, that implements the Fay–Herriot model (Fay and Herriot, 1979, Journal of the American Statistical Association 74: 269–277), which is a small-area estimation technique (Rao and Molina, 2015, Small Area Estimation), in Stata. The Fay–Herriot model improves the precision of area-level direct estimates using area-level covariates. It belongs to the class of linear mixed models with normally distributed error terms. The fayherriot command encompasses options to a) produce out-of-sample predic- tions, b) adjust nonpositive random-effects variance estimates, and c) deal with the violation of model assumptions.

Suggested Citation

  • Christoph Halbmeier & Ann-Kristin Kreutzmann & Timo Schmid & Carsten Schröder, 2019. "The fayherriot command for estimating small-area indicators," Stata Journal, StataCorp LP, vol. 19(3), pages 626-644, September.
    • Handle: RePEc:tsj:stataj:v:19:y:2019:i:3:p:626-644
    DOI: 10.1177/1536867X19874238
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    Cited by:

    1. Natascha Hainbach & Christoph Halbmeier & Timo Schmid & Carsten Schröder, 2019. "A Practical Guide for the Computation of Domain-Level Estimates with the Socio-Economic Panel (and Other Household Surveys)," SOEPpapers on Multidisciplinary Panel Data Research 1055, DIW Berlin, The German Socio-Economic Panel (SOEP).
    2. 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.

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