Some Applications of Panel Data Models in Small Area Estimation

• Authors: Nekrašaitė-Liegė Vilma
• Languages of publication: EN

Abstracts

EN

This study uses a real population from Statistics Lithuania to investigate the performance of different types of estimation strategies. The estimation strategy is a combination of sampling design and estimation design. The sampling designs include equal probability design (SRS) and unequal probability designs (stratified SRS and model-based sampling designs). Design-based direct Horvitz-Thompson, indirect model-assisted GREG estimator and indirect model-based estimator are used to estimate the totals in small area estimation. The underlying panel-type models (linear fixed-effects type or linear random-effects type) are examined in both stages of estimation strategies: sample design and construction of estimators.

Keywords

EN

Small area estimation; panel-type data; model-based; model-assisted

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2011
• Volume: 12
• Issue: 2
• Pages: 265-280

Contributors

author

Nekrašaitė-Liegė Vilma

• Vilnius Gediminas Technical University

References

• HORVITZ, D. G. AND THOMPSON, D. J., (1952). A generalization of sampling without eplacement from a finite universe, Journal of the American Statistical Association, 47:663–685.
• HSIAO, C. (2003). Analysis of Panel Data, Economic Society monographs no. 34, 2nd edition, New York: Cambridge University Press.
• LEHTONEN, R. AND VEIJANEN, A. (2009). Design-based methods of estimation for domains and small areas. Chapter 31 in C.R. Rao and D. Pfeffermann (Eds.) Handbook of Statistics, Vol 29B, Sample Surveys: Inference and Analysis. New York: Elsevier.
• LEHTONEN, R., SÄRNDAL, C.-E. AND VEIJANEN, A. (2003). The effect of model choice in estimation for domains, including small domains, Survey Methodology 29:33-44.
• LEHTONEN, R., SÄRNDAL, C.-E. AND VEIJANEN, A. (2005). Does the model matter? Comparing model-assisted and model-dependent estimators of class frequencies for domains, Statistics in Transition 7:649-673.
• NARAIN, R. D., (1951). On sampling without replacement with varying probabilities, Journal of the Indian Society of AgriculturalStatistics, 3:169–174.
• NEDYALKOVA, D., TILLE, Y., (2008). Optimal sampling and estimation strategies under the linear model, Biometrika 95[3]:521-537.
• NEKRAŠAITĖ-LIEGĖ, V., RADAVIČIUS, M. and RUDYS, T. (2011). Modelbased design in small area estimation. Lithuanian Mathematical Journal. 51[3]:417-424.
• OMRANI, H., GERBER, P. AND BOUSCH. P (2009). Model-Based Small Area Estimation with application to unemployment estimates, World Academy of Science, Engineering and Technology 49, 793-800.
• RAO, J. N. K. (2003). Small Area Estimation. Wiley, New York.
• Rao, J.N.K. (2005). Inferential issues in small area estimation: some new developments. Statistics in Transition, 7, 513-526.
• ROYALL, R. M., (1970). On finite population sampling theory under certain linear regression models Biometrika 57[2]:377-387.
• SAEI, A. and CHAMBERS, R. (2003). Small Area Estimation under Linear and Generalized Linear Mixed Models with Time and Area Effects. Methodology Working Paper No. M03/15. University of Southampton, UK.
• SÄRNDAL, C.-E., SWENSSON, B. and WRETMAN, J. (1992). Model Assisted Survey Sampling, Springer - Verlag, New York.
• SINGH, M.P., GAMBINO, J. and MANTEL, H.J. (1994). Issues and strategies for small area data. Survey Methodology, 20, 314.
• U.S. OFFICE OF MANAGEMENT AND BUDGET (1993). Indirect Estimators in Federal Programs, Statistical Policy Worlang Paper 21, NATIONAL Technical Information Service, Springfield, Virginia.