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Generating correlated random vector involving discrete variables

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  • Qing Xiao

Abstract

For the issue of generating correlated random vector containing discrete variables, one major obstacle is to determine a suitable correlation coefficient ρz in normal space for a specified correlation coefficient ρx. This paper develops a method to solve this problem. First, the double integral evaluated for ρx is transformed into independent standard uniform space, then, a Quasi Monte Carlo method is introduced to calculate the double integral. For a given ρx, an appropriate ρz is determined by a false position method. Compared with existing methodologies, the proposed method is less efficient, but it is relatively easy to implement.

Suggested Citation

  • Qing Xiao, 2017. "Generating correlated random vector involving discrete variables," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(4), pages 1594-1605, February.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:4:p:1594-1605
    DOI: 10.1080/03610926.2015.1024860
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    Cited by:

    1. Alexander D. Knudson & Tomasz J. Kozubowski & Anna K. Panorska & A. Grant Schissler, 2021. "A flexible multivariate model for high-dimensional correlated count data," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-21, December.
    2. Marcin Dec, 2021. "From point through density valuation to individual risk assessment in the discounted cash flows method," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5621-5635, October.
    3. Marcin Dec, 2019. "From point through density valuation to individual risk assessment in the discounted cash flows method," GRAPE Working Papers 35, GRAPE Group for Research in Applied Economics.
    4. Jorge A. Sefair & Oscar Guaje & Andrés L. Medaglia, 2021. "A column-oriented optimization approach for the generation of correlated random vectors," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 777-808, September.
    5. Alessandro Barbiero, 2021. "Inducing a desired value of correlation between two point-scale variables: a two-step procedure using copulas," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 307-334, June.

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