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首页> 外文期刊>Physica, A. Statistical mechanics and its applications >Are your data really Pareto distributed?
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Are your data really Pareto distributed?

机译：您的数据真的是帕累托分布的吗？

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摘要

Pareto distributions, and power laws in general, have demonstrated to be very useful models to describe very different phenomena, from physics to finance. In recent years, the econophysical literature has proposed a large amount of papers and models justifying the presence of power laws in economic data. Most of the times, this Paretianity is inferred from the observation of some plots, such as the Zipf plot and the mean excess plot. If the Zipf plot looks almost linear, then everything is ok and the parameters of the Pareto distribution are estimated. Often with OLS. Unfortunately, as we show in this paper, these heuristic graphical tools are not reliable. To be more exact, we show that only a combination of plots can give some degree of confidence about the real presence of Paretianity in the data. We start by reviewing some of the most important plots, discussing their points of strength and weakness, and then we propose some additional tools that can be used to refine the analysis.
机译：帕累托分布和一般的幂定律已被证明是描述从物理学到金融学的非常不同的现象的非常有用的模型。近年来，经济物理学文献提出了大量证明经济规律中存在幂定律的论文和模型。在大多数情况下，这种Paretianity是通过观察某些图（例如Zipf图和平均超额图）来推断的。如果Zipf图看起来几乎是线性的，那么一切都很好，并且可以估算帕累托分布的参数。经常与OLS一起使用。不幸的是，正如我们在本文中显示的那样，这些启发式图形工具并不可靠。更确切地说，我们表明，只有曲线图的组合才能对数据中真实存在帕累西蒂性给出一定程度的置信度。我们首先回顾一些最重要的图，讨论它们的优缺点，然后提出一些可用于完善分析的附加工具。

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