Perils of Simulation : Parallel Streams and the Case of Stata’s Rnormal Command
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World Bank, Washington, D.C.
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Large-scale simulation-based studies rely on at least three properties of pseudorandom number sequences. Since they behave like random numbers, they can be replicated and generated in parallel. However, there has been some divergence between empirical techniques employing random numbers and the standard battery of tests used to validate them. A random number generator that passes tests for any single stream of random numbers may fail the same tests when it is used to generate multiple streams in parallel. The lack of systematic testing of parallel streams leaves statistical software with important potential vulnerabilities. This paper reveals one such vulnerability in Stata's rnormal function which went unnoticed for almost four years and how this error was detected. Furthermore, the paper discusses practical implications for the use of parallel streams in existing software.
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Applied Mathematics, bootstrap, bug, Command, confidence intervals, cores, Data Analysis, Development Research, Econometrics, empirical methods, Estimators, integer, Kurtosis, Monte Carlo methods, probability, produce, Public Services, random numbers, reliability, Research Working Papers, researchers, sciences, seed, Seeds, Simulation, Simulations, Skewness, Stata, statistical tests, techniques, yields