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.

scholarly journals Enhancing the precision of broad-scale forestland removals estimates with small area estimation techniques

2021 ◽  
Author(s):  
John W Coulston ◽  
P Corey Green ◽  
Philip J Radtke ◽  
Stephen P Prisley ◽  
Evan B Brooks ◽  
...  
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Species Group ◽  
Tree Cover ◽  
Estimation Techniques ◽  
Spatial And Temporal Scales ◽  
Area Estimation ◽  
Forest Inventories ◽  
Southeastern Us ◽  
Increasing Demand

Abstract National Forest Inventories (NFI) are designed to produce unbiased estimates of forest parameters at a variety of scales. These parameters include means and totals of current forest area and volume, as well as components of change such as means and totals of growth and harvest removals. Over the last several decades, there has been a steadily increasing demand for estimates for smaller geographic areas and/or for finer temporal resolutions. However, the current sampling intensities of many NFI and the reliance on design-based estimators often leads to inadequate precision of estimates at these scales. This research focuses on improving the precision of forest removal estimates both in terms of spatial and temporal resolution through the use of small area estimation techniques (SAE). In this application, a Landsat-derived tree cover loss product and the information from mill surveys were used as auxiliary data for area-level SAE. Results from the southeastern US suggest improvements in precision can be realized when using NFI data to make estimates at relatively fine spatial and temporal scales. Specifically, the estimated precision of removal volume estimates by species group and size class was improved when SAE methods were employed over post-stratified, design-based estimates alone. The findings of this research have broad implications for NFI analysts or users interested in providing estimates with increased precision at finer scales than those generally supported by post-stratified estimators.

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.

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

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
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