Click here for search results
Estimation of normal mixtures in a nested error model with an application to small area estimation of poverty and inequality, Volume 1
Author:Elbers, Chris; van der Weide, Roy; Country:World;
Date Stored:2014/07/02Document Date:2014/07/01
Document Type:Policy Research Working PaperSubTopics:Achieving Shared Growth; Econometrics; Economic Theory & Research; Inequality; Statistical & Mathematical Sciences
Language:EnglishRegion:The World Region
Report Number:WPS6962Collection Title:Policy Research working paper ; no. WPS 6962Paper is funded by the Knowledge for Change Program (KCP)
Volume No:1  

Summary: This paper proposes a method for estimating distribution functions that are associated with the nested errors in linear mixed models. The estimator incorporates Empirical Bayes prediction while making minimal assumptions about the shape of the error distributions. The application presented in this paper is the small area estimation of poverty and inequality, although this denotes by no means the only application. Monte-Carlo simulations show that estimates of poverty and inequality can be severely biased when the non-normality of the errors is ignored. The bias can be as high as 2 to 3 percent on a poverty rate of 20 to 30 percent. Most of this bias is resolved when using the proposed estimator. The approach is applicable to both survey-to-census and survey-to-survey prediction.

Official Documents
Official, scanned versions of documents (may include signatures, etc.)
File TypeDescriptionFile Size (mb)
PDF 33 pagesOfficial version*2.31 (approx.)
TextText version**
How To Order

* The official version is derived from scanning the final, paper copy of the document and is the official,
archived version including all signatures, charts, etc.
** The text version is the OCR text of the final scanned version and is not an accurate representation of the final text.
It is provided solely to benefit users with slow connectivity.

Permanent URL for this page: