Fast Bayesian modelling in Stan using the StataStan program
Robert Grant · RePEc: Research Papers in Economics · 2015
Over the last three years, a new package for Bayesian modelling called Stan (after Stanislaw Ulam, co-author of the Metropolis algorithm) has been developing quickly and making an impact on computing for complex Bayesian models. By translating the model into C++ and then compiling that, it can run much faster than BUGS. A particular benefit is for simulation studies, because the model only needs to be compiled once. Furthermore, it includes a much faster and better mixing algorithm (NUTS: the No U-Turn Sampler), especially for correlated parameters that Gibbs samplers like BUGS cope with badly. I present a program StataStan, which sends your data and specifications to Stan, displays results, and can read the chains of samples back into Stata. There are also specific commands to run the commonly used models in the BUGS and Stan user manuals with your own data, avoiding the need to write the Stan model.