The Use of the Robust GREG Estimator to Estimate Small Trade Firms

Grażyna Dehnel

doi:10.18778/0208-6018.334.03

Authors

Grażyna Dehnel Poznań University of Economics and Business, Faculty of Informatics and Electronic Economy, Department of Statistics

DOI:

https://doi.org/10.18778/0208-6018.334.03

Keywords:

robust estimation, business statistics, small area estimation, GREG

Abstract

In the face of dynamic changes in the economy, there is a growing demand for multivariate statistics for cross‑classified domains. In economic statistics, this demand poses a particular challenge owing to the unique character of the population of enterprises, which is what motivates the search for estimation methods that can exploit administrative sources to a greater extent. The adoption of new solutions in this area is expected to increase the scope of statistical outputs and improve the efficiency of estimates. The purpose of the presented study is to test the application of the robust GREG estimator based on the LS method and least median of squares regression to estimate characteristics of small trade firms operating in 2012. The estimation process is supported with delayed variables from administrative registers used as auxiliary variables. The paper refers to small area estimation methods. The variables of interest are estimated at the low level of aggregation represented by cross‑section province and NUTS 2.

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References

Bracha C. (2004), Estymacja danych z badania aktywności ekonomicznej ludności na poziomie powiatów dla lat 1995–2002, GUS, Warszawa.

Chambers R., Kokic P., Smigh P., Cruddas M. (2000), Winsorization for Identifying and Treating Outliers in Business Surveys, Proceedings of the Second International Conference on Establishment Surveys, American Statistical Association, Alexandria.

Dehnel G. (2014), Winsorization Methods in Polish Business Survey, “Statistics in Transition – New Series”, vol. 15, no. 1, pp. 97–110, http://pts.stat.gov.pl/czasopisma/statistics‑in‑transition/ [accessed: 25.11.2017].

Dehnel G. (2015), Rejestr podatkowy oraz rejestr ZUS jako źródło informacji dodatkowej dla statystyki gospodarczej – możliwości i ograniczenia, [in:] K. Jajuga, M. Walesiak (eds.), Taksonomia 24. Klasyfikacji i analiza danych – teoria i zastosowania, Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu, Wrocław.

Gross W.F., Bode G., Taylor J.M., Lloyd-Smith C.W. (1986), Some finite population estimators which reduce the contribution of outliers, Proceedings of the Pacific Statistical Conference, 20–24 May 1985, Auckland.

GUS (2014), Działalność przedsiębiorstw niefinansowych w 2012 roku, Warszawa.

GUS (2015), Małe i średnie przedsiębiorstwa niefinansowe w latach 2009–2013, Warszawa.

GUS (2016), Wykorzystanie danych administracyjnych w badaniu: Ocena bieżącej działalności gospodarczej przedsiębiorstw, Warszawa.

Horvitz D.G., Thompson D.J. (1952), A Generalization of Sampling without Replacement from a Finite Universe, “Journal of the American Statistical Association”, vol. 47, pp. 663–685.

Preston J., Mackin C. (2002), Winsorization for Generalised Regression Estimation, Paper for the Methodological Advisory Committee, Australian Bureau Of Statistics, Canberra.

Rao J.N.K., Molina I. (2015), Small Area Estimation, Wiley, Hoboken, doi: 10.1002/9781118735855.

Rousseeuw P.J., Leroy P.M. (2003), Robust Regression and Outlier Detection, Wiley-Interscience, Hoboken.