Hello, all. I have long been a theoretical fan of Bayesian statistics, especially since I read McElreath’s Statistical Rethinking, and I finally want to get my feat wet with some actual modelling.
What has drawn me to Stan most of all is that it seems to perform time-series analysis pretty well, as opposed to every other Bayesian library out there (WinBugs, Pymc3, JAGS, etc…)? One well-developed time-series example can be found here, https://khakieconomics.github.io/2016/11/27/Hierarchical-priors-for-panel-vector-autoregressions.html .
What would be the reason for Stan’s apparent ease with time-series estimation? The only information on Stan’s samplers that I have been able to find are that it uses both HMC and NUTS, but other libraries (such as pymc3) also use them, but grind to a halt even with basic AR(1) models, or do not converge.
I would have guessed that Stan uses a special sampler for time-series, such as Sequential Monte Carlo (SMC), but that does not seem to be the case (?) .
Any statistical information on why Stan is so good in this area would be very helpful for me.