This package implements the Sequential Monte Carlo (SMC) sampling algorithm, an alternative to Metropolis Hastings Markov Chain Monte Carlo sampling for approximating posterior distributions. The SMC algorithm implemented here is based upon and extends Edward Herbst and Frank Schorfheide's paper "Sequential Monte Carlo Sampling for DSGE Models" and the code accompanying their book, Bayesian Estimation of DSGE Models. More information and the original MATLAB scripts from which this code was derived can be found at Frank Schorfheide's website.
Our implementation features an adaptive schedule and what we term generalized tempering for "online" estimation, as outlined in our recent paper, "Online Estimation of DSGE Models." For a broad overview of the algorithm, one may refer to the following Liberty Street Economics article.
Comments and suggestions are welcome, and best submitted as either an issue or a pull request. ☝️
SMC.jl is a registered Julia package in the General registry, compatible with Julia v1.x. To install it, open your Julia REPL, type ] to enter the package manager, and run
pkg> add SMCThe package requires our auxiliary package, ModelConstructors.jl, which contains useful data structures for creating custom models (e.g. Parameter, State, Observable, Setting types).
For examples of how to set up a model in the form SMC can estimate, see scripts in the examples/ folder.
The SMC.jl package is not precompiled by default because when running code in parallel, we want to re-compile
the copy of SMC.jl on each processor to guarantee the right version of the code is being used. If users do not
anticipate using parallelism, then users ought to change the first line of src/SMC.jl from
isdefined(Base, :__precompile__) && __precompile__(false)
to
isdefined(Base, :__precompile__) && __precompile__(true)
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