Improving small area estimation by combining surveys: new perspectives in regional statistics
AbstractA national survey designed for estimating a specific population quantity is sometimes used for estimation of this quantity also for a small area, such as a province. Budget constraints do not allow a greater sample size for the small area, and so other means of improving estimation have to be devised. We investigate such methods and assess them by a Monte Carlo study. We explore how a complementary survey can be exploited in small area estimation. We use the context of the Spanish Labour Force Survey (EPA) and the Barometer in Spain for our study.
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Bibliographic InfoPaper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 969.
Date of creation: Jun 2006
Date of revision:
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Web page: http://www.econ.upf.edu/
Composite estimator; complementary survey; mean squared error; official statistics; regional statistics; small area;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-07-09 (All new papers)
- NEP-ECM-2006-07-09 (Econometrics)
- NEP-GEO-2006-07-09 (Economic Geography)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Àlex Costa & Albert Satorra & Eva Ventura, 2003. "Using composite estimators to improve both domain and total area estimation," Economics Working Papers 731, Department of Economics and Business, Universitat Pompeu Fabra.
- Àlex Costa & Albert Satorra & Eva Ventura, 2003. "An empirical evaluation of small area estimators," Economics Working Papers 674, Department of Economics and Business, Universitat Pompeu Fabra, revised Jun 2003.
- Alex Costa & Albert Satorra & Eva Ventura, 2003. "An Empirical Evaluation of Five Small Area Estimators," General Economics and Teaching 0312003, EconWPA.
- Nicholas T. Longford, 2004. "Missing data and small area estimation in the UK Labour Force Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 341-373.
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