Voineagu, Vergil and Caragea, Nicoleta and Pisica, Silvia (2013): Estimating International Migration on the Base of Small Area Techniques.
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Abstract
Population migration flow is a component of population facing difficulties in measuring in the inter-census period of time. The rationale of this study is that Romanian statistics on international migration flows are of very poor quality, the availability of data on past trends being strongly limited, provided only from administrative sources. For this reason, in the inter-census period, the variable of interest is provided by the labour force survey available at national and regional level every quarter of the year since 2004. The smaller disaggregation like localities level using direct estimators conducts to results of unreliable estimates and will surely lead to higher standard error and consequently, high coefficients of variation. The main reason for this is the insufficient number of respondents or no respondent at all in a small domain. Small area estimation techniques are able to carry out the estimation at the localities level (NUTS 5). The main purpose is to provide methods able to estimate the population in Romania, based on the Labour Force Survey and also the results of 2002, respectively 2011 population census.
Item Type: | MPRA Paper |
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Original Title: | Estimating International Migration on the Base of Small Area Techniques |
English Title: | Estimating International Migration on the Base of Small Area Techniques |
Language: | English |
Keywords: | international migration, population, demography, statistics, small area estimation |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods |
Item ID: | 48775 |
Depositing User: | Dr. Nicoleta Caragea |
Date Deposited: | 01 Aug 2013 19:56 |
Last Modified: | 01 Oct 2019 20:21 |
References: | [1] Baltagi, H.B., (2011), Econometrics, Springer Publisher, ISBN 978-3-642-20058-8. [2] Battese, G. E., Harter, R. M. & Fuller, W. A. (1988), An error-components model for prediction of county crop areas using survey and satellite data, Journal of the American Statistical Association, 83, 28-36 [3] Breidenbach, J. and Astrup, R. (submitted 2011), Small area estimation of forest attributes in the Norwegian National Forest Inventory. European Journal of Forest Research. [4] C.-E. S¨arndal, B. Swensson, and J. Wretman. Model Assisted Survey Sampling.Springer-Verlag Inc., New York, 1992. [5] Caragea, N., Alexandru, A.C., Dobre, A.M. (2012), Bringing New Opportunities to Develop Statistical Software and Data Analysis Tools in Romania, The Proceedings of the VIth International Conference on Globalization and Higher Education in Economics and Business Administration, ISBN: 978-973-703-766-4, pp.450-456. [6] Gomez-Rubio (2008), Tutorial on small area estimation, UseR conference 2008, August 12-14, Technische Universitat Dortmund, Germany. [7] Jula, N. and Jula, D., (2009), Modelare economica. Modele econometrice si de optimizare., Mustang Publisher, ISBN 978-606-8058-14-6 [8] Michele D’Al ´o, Loredana Di Consiglio, Stefano Falorsi, Fabrizio Solari, Course on Small Area Estimation, ESSnet Project on SAE, Small area estimation [9] R Development Core Team (2005). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL: http://www.R-project.org. [10] Rao, J.N.K. (2003), Small area estimation. [11] Sarndal, C. (1984), Design-consistent versus model-dependent estimation for small domains, Journal of the American Statistical Association, JSTOR, 624-631 [12] Schoch, T. (2011), rsae: Robust Small Area Estimation. R package version 0.1-3. [13] Voineagu, V., Pisica, S., Caragea, N., (2012) Forecasting Monthly Unemployment by Econometric Smoothing Techniques, http://www.ecocyb.ase.ro/32012/Virgil%20Voineagu.pdf |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/48775 |