Robust small area estimation under semi-parametric mixed models

2013 ◽  
Vol 42 (1) ◽  
pp. 126-141 ◽  
Author(s):  
Jon N. K. Rao ◽  
Sanjoy K. Sinha ◽  
Laura Dumitrescu
Keyword(s):  
Mixed Models ◽  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation

The fayherriot command for estimating small-area indicators

2019 ◽  
Vol 19 (3) ◽  
pp. 626-644
Author(s):  
Christoph Halbmeier ◽  
Ann-Kristin Kreutzmann ◽  
Timo Schmid ◽  
Carsten Schröder
Keyword(s):  
Mixed Models ◽  
Small Area ◽  
Small Area Estimation ◽  
American Statistical Association ◽  
Estimation Technique ◽  
Statistical Association ◽  
Area Estimation ◽  
Out Of Sample ◽  
Error Terms ◽  
Variance Estimates

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 predictions, b) adjust nonpositive random-effects variance estimates, and c) deal with the violation of model assumptions.

scholarly journals Small area estimation with mixed models: a review

2020 ◽  
Vol 3 (2) ◽  
pp. 693-720
Author(s):  
Shonosuke Sugasawa ◽  
Tatsuya Kubokawa
Keyword(s):  
Mixed Models ◽  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation

On model-based nowcasting for highly disaggregated levels

2020 ◽  
pp. 1-14
Author(s):  
María-Dolores Esteban ◽  
Domingo Morales ◽  
Agustin Pérez ◽  
Stefan Sperlich
Keyword(s):  
International Organizations ◽  
Mixed Models ◽  
Small Area ◽  
Missing Values ◽  
Small Area Estimation ◽  
Natural Extension ◽  
Temporal Correlation ◽  
Random Parameter ◽  
Area Estimation ◽  
Increasing Demand

Nowadays, national and international organizations experience an increasing demand for timely and disaggregated socio-economic indicators. More recently, this demand extends to the request for nowcasting indicators. Small Area Estimation has a long tradition in indicator prediction for high levels of disaggregation; but when speaking of ‘prediction’, this notation refers to the fact that the centre of interest is a random parameter. Prediction of future values, or similarly, nowcasting has hardly been studied so far. Yet, mixed models based Small Area Estimation is designed for imputing (missing) values, and these models can easily account for temporal correlation. Therefore, model assisted nowcasting would be a natural extension. This article reviews existing methods under this perspective to highlight the necessary ingredients, and then propose nowcasting procedures for highly disaggregated indicators that could already be used with the today’s available software.

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