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Random Number Generation in gretl

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  • Yalta, A. Talha
  • Schreiber, Sven

Abstract

The increasing popularity and complexity of random number intensive methods such as simulation and bootstrapping in econometrics requires researchers to have a good grasp of random number generation in general, and the specific generators that they employ in particular. Here, we discuss the random number generation options, their specifications, and their implementations in gretl. We also assess the performance and the reliability of gretl in this department by conducting extensive empirical testing using the TestU01 library. Our results show that the available alternatives are soundly implemented and should be sufficient for most econometric applications.

Suggested Citation

  • Yalta, A. Talha & Schreiber, Sven, 2012. "Random Number Generation in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(c01).
    • Handle: RePEc:jss:jstsof:v:050:c01
    DOI: http://hdl.handle.net/10.18637/jss.v050.c01
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    References listed on IDEAS

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    Cited by:

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    3. Christine Amsler & Alecos Papadopoulos & Peter Schmidt, 2021. "Evaluating the cdf of the Skew Normal distribution," Empirical Economics, Springer, vol. 60(6), pages 3171-3202, June.

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