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Portable random number generators

Author

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  • Gerald P. Dwyer
  • K. B. Williams

Abstract

Computers are deterministic devices, and a computer-generated random number is a contradiction in terms. As a result, computer-generated pseudorandom numbers are fraught with peril for the unwary. We summarize much that is known about the most well-known pseudorandom number generators: congruential generators. We also provide machine-independent programs to implement the generators in any language that has 32-bit signed integers-for example C, C++, and FORTRAN. Based on an extensive search, we provide parameter values better than those previously available.

Suggested Citation

  • Gerald P. Dwyer & K. B. Williams, 1999. "Portable random number generators," FRB Atlanta Working Paper 99-14, Federal Reserve Bank of Atlanta.
  • Handle: RePEc:fip:fedawp:99-14
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    References listed on IDEAS

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    1. Sangjoon Kim, Neil Shephard & Siddhartha Chib, "undated". "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers W26, revised version of W, Economics Group, Nuffield College, University of Oxford.
    2. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
    3. McCullough, B D, 1999. "Econometric Software Reliability: EViews, LIMDEP, SHAZAM and TSP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(2), pages 191-202, March-Apr.
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    Cited by:

    1. Tang, Hui-Chin, 2006. "Theoretical analyses of forward and backward heuristics of multiple recursive random number generators," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1760-1768, November.

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    Keywords

    Programming (Mathematics) ; Computers;

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