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PySDTest: a Python/Stata Package for Stochastic Dominance Tests

Kyungho Lee, Yoon-Jae Whang

arXiv 20 Jul 2023 · Econometrics · 1 citations (OpenAlex)

arXiv:2307.10694 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We introduce PySDTest, a Python/Stata package for statistical tests of stochastic dominance. PySDTest implements various testing procedures such as Barrett and Donald (2003), Linton et al. (2005), Linton et al. (2010), and Donald and Hsu (2016), along with their extensions. Users can flexibly combine several resampling methods and test statistics, including the numerical delta method (D\"umbgen, 1993; Hong and Li, 2018; Fang and Santos, 2019). The package allows for testing advanced hypotheses on stochastic dominance relations, such as stochastic maximality among multiple prospects. We first provide an overview of the concepts of stochastic dominance and testing methods. Then, we offer practical guidance for using the package and the Stata command pysdtest. We apply PySDTest to investigate the portfolio choice problem between the daily returns of Bitcoin and the S&P 500 index as an empirical illustration. Our findings indicate that the S&P 500 index returns second-order stochastically dominate the Bitcoin returns.

Citation extraction

33
references
59
in-text mentions
33
distinct cited
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main-text words

appendix boundary found by appendix_titled_section at “A. Appendix” · 96% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Hong and Li (2018) The numerical delta method1.00083100%
2Fang and Santos (2019) Inference on directionally differentiable functions1.00073100%
3Dümbgen (1993) On nondifferentiable functions and the bootstrap0.84333100%
4Davidson and Duclos (2000) Statistical inference for stochastic dominance and for the measurement of poverty and inequality0.73732100%
5Klecan, McFadden and McFadden (1991) A robust test for stochastic dominance0.73732100%
6Barrett and Donald (2003) Consistent tests for stochastic dominance0.64422100%
7Donald and Hsu (2016) Improving the power of tests of stochastic dominance0.64422100%
8Linton, Maasoumi and Whang (2005) Consistent testing for stochastic dominance under general sampling schemes self0.64422100%
9Linton, Song and Whang (2010) An improved bootstrap test of stochastic dominance self0.64422100%
10Whang (2019) self0.64422100%

Showing the top 10 of 33 scored citations.