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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.