Politická ekonomie 2011, 59(1):105-129 | DOI: 10.18267/j.polek.774

Aplikace robustní regrese v analýze komparativních cenových hladin zemí Evropské unie

Dagmar Blatná
Vysoká škola ekonomická v Praze.

Robust Regression in Analysis of Comparative Price Levels of EU Countries

The values of comparative price levels vary greatly in individual EU countries and depend on many different economic factors. The EU countries were divided into two distinguishable groups. Both OLS and robust regressions were used to analyze the influence of the comparative price levels on selected indicators. If outliers and leverage points were identified using robust outliers' detection and the results of the OLS and robust fits differed significantly, the robust fits are preferred. When differences were not significant, the OLS fits can be used. Models for 27 EU countries and groups of countries differ significantly with respect to indicators included.

Keywords: comparative price levels, EU countries, OLS regression, robust regression, robust outliers´ detection
JEL classification: E31, O11, O57

Published: February 1, 2011  Show citation

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Blatná, D. (2011). Robust Regression in Analysis of Comparative Price Levels of EU Countries. Politická ekonomie59(1), 105-129. doi: 10.18267/j.polek.774
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References

  1. ANTOCH, J.; VORLÍČKOVÁ, D. 1992. Vybrané metody statistické analýzy dat. Praha: Academia, 1992. ISBN 80-200-0204-9.
  2. BLATNÁ, D. 1996. Neparametrické metody. Praha: VŠE v Praze, 1996. ISBN 80-7089-607-3.
  3. CHEN, C. 2002. Robust Regression and Outlier Detection with the ROBUSTREG procedure. SUGI Paper 265-27. SAS Institute Inc., Cary, NC. 2002.
  4. FRAIT, J.; KOMÁREK, L. 2001. Na cestě do Evropské unie: nominální a reálná konvergence v tranzitivních ekonomikách. Finance a úvěr. 2001, Vol. 51, No. 6, pp. 314-330. ISSN 0015-1920.
  5. HADI, A. S.; SIMONOFF, J. S. 1993. Procedures for the Identification of Multiple Outliers in Linear Models. Journal of the American Statistical Association. 1993, Vol 88, No. 424, pp. 1264-1272. Go to original source...
  6. HEBÁK, P.; HUSTOPECKÝ, J.; PECÁKOVÁ, I.; PRŮŠA, M.; ŘEZANKOVÁ, H.; SVOBODOVÁ, A.; VLACH, P. 2005. Vícerozměrné statistické metody (3). Praha: Informatorium, 2005. ISBN 80-7333-039-3.
  7. HEBÁK, P.; HUSTOPECKÝ, J.; MALÁ, I. 2005. Vícerozměrné statistické metody (2). Praha: Informatorium, 2005. ISBN 80-7333-036-9.
  8. HUBERT, M.; ROUSSEEUW, P. J.; Van AELST. 2008. High-Breakdown Robust Multivariate Methods. Statistical Science. 2008, Vol 23, No. 1, pp. 92-119. ISSN0883-4237 Go to original source...
  9. HUŠEK, R. 2007. Ekonometrická analýza. Praha: Nakladatelství Oeconomica, 2007. ISBN 978-80-245-1300-3.
  10. MARONNA, R. A.; MARTIN, R. D.; YOHAI, V. J. 2006. Robust Statistics. Theory and Methods. London: J Wiley, 2006. ISBN-13 978-0-470-01092-1
  11. OLIVE, D. J. 2002. Applications of Robust Distances for Regression. Technometrics. 2002, No. 44, pp. 64-71. Go to original source...
  12. RAO, C. R.; TOUTENBURG, H. 1995. Linear Models. Least Squqres and Alternatives. New York: Springer-Verlag, 1995. ISBN 0-387-95462-8.
  13. ROUSSEEUW, P. J. 1984. Least Median of Squares Regression. Journal of the American Statistical Association. 1984, No. 79, pp. 878-880. Go to original source...
  14. ROUSSEEUW, P. J.; LEROY, A. M. 2003. Robust Regression and Outlier Detection. New Jersey: J. Wiley, 2003. ISBN 0-471-48855-0.
  15. ROUSSEEUW, P. J.; VAN ZOMEREN, B. C. 1990. Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association. 1990, No. 85, pp. 633-639. Go to original source...
  16. RUPPERT, D.; CARROLL, R. J. 1990. Trimmed Least Squares Estimation in the Linear Model. Journal of the American Statistical Association. 1990, No. 75, pp. 828-838. Go to original source...
  17. SWALLOW, W. H.; KIANIFARD, F. 1996. Using robust scale estimates in detecting multiple outliers in linear regression. Biometrics. 1996, No. 52, pp. 545-556. Go to original source...
  18. ŠAROCH, S.; ŽÁK, M. (ed.) 2004. Česká ekonomika a ekonomická teorie. 1. vyd. Praha : Academia, 2004. 266 s. ISBN 80-200-1129-3.
  19. VINTROVÁ, R.; ŽĎÁREK, V. 2006. Konvergence České republiky a Slovenské republiky - současný stav a vybrané problémy. Ekonomický časopis. 2006, č. 5, s. 468-489. ISSN 0013-3035.
  20. VINTROVÁ, R.; ŽĎÁREK, V. 2006. Nové členské země Evropské unie a příprava na přijetí společné měny. Praha: Bulletin CES VŠEM, č. 17/2006, s. 1-3. ISSN 1801-1578.
  21. VINTROVÁ, R. 2010. Cenová a mzdová konvergence nových členských zemí EU. Praha: Bulletin CES VŠEM, 1/2010, s. 5-7. ISSN 1801-6871.
  22. UCLA. Academy Technology Services. Regression with SAS. http://www.ats.ucla.edu/stat/sas
  23. YOHAI, V. J. 1987. High Breakdown-point and High Efficiency Robust Estimates for Regression. The Annals of Statistics. 1987, Vol. 15, No 20, pp. 642-656. Go to original source...
  24. ZVÁRA, K. 1989. Regresní analýza. Praha: Academia, 1989. ISBN 80-200-0125-5.
  25. ŽDÁREK, V. 2006. Nominální konvergence v České republice. Praha: Bulletin CES VŠEM, č. 23/ 2006, s. 5-6. ISSN 1801-6871.
  26. ŽĎÁREK, V. 2010. Cenová konvergence nových členských zemí EU- strukturální pohled. Praha: Bulletin CES VŠEM, č. 2/2010. ISSN 1801-1578.
  27. ŽĎÁREK, V.; ŠINDEL, J. 2007. Real and Nominal Convergence and the New EU Members States - Actual State and Implication. Prague Economic Papers. 2007, Vol. 14, No. 3, pp.195-219. ISSN 1210-0455. Go to original source...

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