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Abstracts
Functional exploratory techniques are applied in the analysis of an unemployment rate. The rate is smoothed into differentiable function in order to facilitate the analysis. The main aim of the analysis is assigned to find out the unemployment curves which do not follow the same pattern as that of the other ones.
Year
Volume
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Pages
180-189
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Dates
published
2013
Contributors
author
- Department of Econometrics and Statistics Warsaw University of Life Sciences – SGGW , stanislaw_jaworski@sggw.pl
References
- Burgen E., Meyer B., and Tasci M. (2012) An Elusive Relation between Unemployment and GDP Growth: Okun’s Law. Cleveland Federal Reserve Economic Trends.
- Cuevas A., Febrero-Bande M., Fraiman R. (2007) Robust Estimation and Clasification for Functional Data via Projction-Based Depth Notions, Computational Statistics, 22(3), 481-496.
- Febrero-Bande M., Galeano P., Gonzales-Manteiga W. (2008) Outlier Detection in Functional Data by Depth Measures, with Application to Identity Abnormal NOx Levels, Environmetrics, 19(4), 331-345
- Ferraty F., Vieu P. (2006) Nonparametric Functional Data Analysis, Springer Series in Statistics. Springer-Verlag, New York. Theory and practice.
- Furmańczyk K., Jaworski S. (2012) Unemployment rate for various countries since 2005 to 2012: comparison of its level and pace using functional principal analysis. Quntitative Methods in Economics, Vol. XIII, No 2, pp. 40-47.
- Horváth L, Kokoszka P. (2012) Inference for Functional Data with Applications, Springer Series in Statistics
- Ramsay J.O., Silverman B. W. (2005) Functional Data Analysis. Second Edition, Springer, NY.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-49d09d50-ff08-45a3-a0d5-bef5f2c4454f