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Copula-Based Synthetic Data Generation in Firm-Size Variables

Author

Listed:
  • Shouji Fujimoto

    (Kanazawa Gakuin University)

  • Atushi Ishikawa

    (Kanazawa Gakuin University)

  • Takayuki Mizuno

    (National Institute of Informatics)

Abstract

Using the survival Clayton copula, we propose a method for generating synthetic data on such firm-size variables as operating revenues and the number of employees. Synthetic data must satisfy two stylized facts on firm-size statistics. First, firm-size distributions have power-law tails. Second, there should be a Gibrat’s law for the ratio of two different firm-size variables. With the survival Clayton copula, we introduce random variables whose marginal distributions are uniform on the interval from 0 to 1, and transform them to obey power-law distributions. The resulting variables satisfy the two stylized facts.

Suggested Citation

  • Shouji Fujimoto & Atushi Ishikawa & Takayuki Mizuno, 2022. "Copula-Based Synthetic Data Generation in Firm-Size Variables," The Review of Socionetwork Strategies, Springer, vol. 16(2), pages 479-492, October.
    • Handle: RePEc:spr:trosos:v:16:y:2022:i:2:d:10.1007_s12626-022-00128-6
    DOI: 10.1007/s12626-022-00128-6
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    References listed on IDEAS

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    1. Fujimoto, Shouji & Ishikawa, Atushi & Mizuno, Takayuki & Watanabe, Tsutomu, 2011. "A new method for measuring tail exponents of firm size distributions," Economics Discussion Papers 2011-29, Kiel Institute for the World Economy (IfW Kiel).
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    3. Shouji Fujimoto & Takayuki Mizuno & Atushi Ishikawa, 2022. "Interpolation of non-random missing values in financial statements’ big data using CatBoost," Journal of Computational Social Science, Springer, vol. 5(2), pages 1281-1301, November.
    4. Fujimoto, Shouji & Ishikawa, Atushi & Mizuno, Takayuki & Watanabe, Tsutomu, 2011. "A new method for measuring tail exponents of firm size distributions," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 5, pages 1-20.
    5. Fujiwara, Yoshi & Di Guilmi, Corrado & Aoyama, Hideaki & Gallegati, Mauro & Souma, Wataru, 2004. "Do Pareto–Zipf and Gibrat laws hold true? An analysis with European firms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(1), pages 197-216.
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    7. Hofert, Marius & Maechler, Martin, 2011. "Nested Archimedean Copulas Meet R: The nacopula Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i09).
    8. Sergey I. Nikolenko, 2021. "Synthetic Data for Deep Learning," Springer Optimization and Its Applications, Springer, number 978-3-030-75178-4, June.
    9. Fujimoto, S. & Ishikawa, A. & Mizuno, T. & Watanabe, T. & 渡辺, 努 & ワタナベ, ツトム, 2011. "A New Method for Measuring Tail Exponents of Firm Size Distributions," Working Paper Series 7, Center for Interfirm Network, Institute of Economic Research, Hitotsubashi University.
    10. Kojadinovic, Ivan & Yan, Jun, 2010. "Modeling Multivariate Distributions with Continuous Margins Using the copula R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i09).
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    1. Takayuki Mizuno & Takaaki Ohnishi & Ryohei Hisano & Hiroshi Iyetomi & Tsutomu Watanabe, 2022. "Preface of Special Issue on Data Science Questing for a Better Society," The Review of Socionetwork Strategies, Springer, vol. 16(2), pages 333-335, October.

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