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Ab Initio Analysis of All Income Society Classes in the European Union

100%
Jagielski M., Kutner R.
Acta Physica Polonica A
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2013
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vol. 123
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issue 3
538-541
EN
We found a unified formula for description of the household incomes of all society classes, for instance, of those of the European Union in year 2007. This formula is a stationary solution of the threshold Fokker-Planck equation (derived from the threshold nonlinear Langevin one). The formula is more general than the well known that of Yakovenko et al. because it satisfactorily describes not only household incomes of low- and medium-income society classes but also the household incomes of the high-income society class.
2
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Study of Households' Income in Poland by Using the Statistical Physics Approach

100%
Jagielski M., Kutner R.
|
|
issue 4
615-618
EN
At the end of 19th century Vilfredo Pareto, as the first tried by using power-laws to describe wealth and income distributions in society. We applied early works of Pareto as well as Gibrat (i.e. laws of Pareto and rules of proportionate growth, respectively). Furthermore, we used recent and advanced models: the Generalised Lotka-Volterra model and collision models. By using empirical data for annual income of Polish households, e.g. for years 2003 and 2006, the comparison with these theoretical models was successfully made. The surprisingly good agreements with Pareto distribution were obtained, where Pareto exponents near the cubic law were found for middle class. For the low class very good agreement with prediction of the cumulative log-normal distribution was gained. Hence, it was possible to establish the border between low and middle society levels. The same was possible for the border between high and middle classes as the ranking for the former follows (to some extent) the Zipf law.
3
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Preliminary Comparison of Households' Income in Poland with European Union and United States Ones by Using the Statistical Physics Methods

81%
Jagielski M., Kutner R., Pęczkowski M.
Acta Physica Polonica A
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2012
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vol. 121
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issue 2B
B-47-B-49
EN
In this work we compared the empirical data of annual income of Polish and European households as well as annual income of individuals in United States (e.g. for years 2006 and 2008) with predictions of the most popular theoretical models. Particularly good agreements with Pareto distribution and prediction of the Yakovenko model were obtained. For the low society class well agreement with prediction of the cumulative exponential distribution was gained. However, it turned out that the cumulative distribution of annual income of Polish households can be described quite well by the Generalised Lotka-Volterra model.
4
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Comparative Analysis of Income Distributions in the European Union and the United States

81%
Jagielski M., Duczmal R., Kutner R.
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EN
We prove that the most rafined approach - our extension of the Yakovenko et al. model - is a universal in the sense that it well describes both household incomes in the European Union and the individual incomes in the United States for all income social classes. This prove was based on our comparative study of various kinds of incomes. The study constitutes a basis for the finding of an impact of the recent world-wide financial crisis on the volatility of various temporary Pareto exponents and on other parameters of the model.
5
Content available remote

Statistical Collapse of Excessive Market Losses

71%
Denys M., Jagielski M., Gubiec T., Kutner R., Stanley H.
Acta Physica Polonica A
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2016
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vol. 129
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issue 5
913-916
EN
We analytically derive superstatistics (or complex statistics) that accurately model empirical market activity data (supplied by Bogachev, Ludescher, Tsallis, and Bunde) exhibiting transition thresholds. We measure the interevent times between excessive losses (that is, greater than some threshold) and use the mean interevent time as a control variable to derive a universal description of empirical data collapse. Our superstatistic value is a power-law corrected by the lower incomplete gamma function, which asymptotically tends toward robustness but initially gives an exponential. We find that the scaling shape exponent that drives our superstatistics subordinates themselves and a "superscaling" configuration emerges.
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