MOpt.jl

Simulated Method of Moments for Julia
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41 Stars
Updated Last
6 Months Ago
Started In
July 2014

SMM.jl: Simulated Method of Moments for Julia

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Notice: this package was previously called MomentOpt.jl.

This package provides a Julia infrastructure for Simulated Method of Moments estimation, or other problems where we want to optimize a non-differentiable objective function. The setup is suitable for all kinds of likelihood-free estimators - in general, those require evaluating the objective at many regions. The user can supply their own algorithms for generating successive new parameter guesses. We provide a set of MCMC template algorithms. The code can be run in serial or on a cluster.

Installation

In your julia REPL, type

] add SMM

Documentation

Examples

Please check out a fully worked example in src/Examples.jl.

Here is a working session comparing serial vs parallel performance on a test objective function. Notice that parallel performance hinges on the objective function being reasonably expensive to compute (at least 0.1 seconds per function evaluation) - otherwise the overhead from data transfer is just too high.

julia> using SMM
[ Info: Precompiling SMM [bc769cb7-f249-5bba-802a-79f18cb247ec]

julia> x = SMM.serialNormal(2,200,slow = true)
[ Info: Starting estimation loop.
Progress: 100%|██████████████████████████████████████████████| Time: 0:01:05
┌ Warning: could not find 'filename' in algo.opts - not saving anything
└ @ SMM ~/.julia/dev/SMM/src/mopt/AlgoAbstract.jl:67
[ Info: Done with estimation after 1.1 minutes
summary(MA) = 3×5 DataFrame
 Row │ id     acc_rate   perc_exchanged  exchanged_most_with  best_val
     │ Int64  Float64    Float64         Int64                Float64
─────┼──────────────────────────────────────────────────────────────────
   11  0.0793651             5.5                    2  0.0023224
   22  0.0819672             8.5                    1  0.0126754
   33  0.115183              4.5                    2  0.0145625
(
BGP Algorithm with 3 BGPChains
============================

Algorithm
---------
Current iteration: 200
Number of params to estimate: 2
Number of moments to match: 2

, Plot{Plots.GRBackend() n=2}, Plot{Plots.GRBackend() n=3})

julia> using Distributed

julia> addprocs(2, exeflags="--project=.")  # you don't need the `exeflag` if you `add`ed the package regularly!
2-element Array{Int64,1}:
 2
 3

julia> @everywhere using SMM

julia> x = SMM.serialNormal(2,200,slow = true)
[ Info: Starting estimation loop.
Progress: 100%|█████████████████████████████████████████████| Time: 0:00:49
┌ Warning: could not find 'filename' in algo.opts - not saving anything
└ @ SMM ~/.julia/dev/SMM/src/mopt/AlgoAbstract.jl:67
[ Info: Done with estimation after 0.8 minutes
summary(MA) = 3×5 DataFrame
 Row │ id     acc_rate   perc_exchanged  exchanged_most_with  best_val
     │ Int64  Float64    Float64         Int64                Float64
─────┼───────────────────────────────────────────────────────────────────
   11  0.117347              2.0                    2  0.00246371
   22  0.0899471             5.5                    3  0.103399
   33  0.161458              4.0                    2  0.139263
(
BGP Algorithm with 3 BGPChains
============================

Algorithm
---------
Current iteration: 200
Number of params to estimate: 2
Number of moments to match: 2

, Plot{Plots.GRBackend() n=2}, Plot{Plots.GRBackend() n=3})

Contributing

We encourage user contributions. Please submit a pull request for any improvements you would like to suggest, or a new algorithm you implemented.

New algorithms:

  • You can model your algo on the basis of src/AlgoBGP.jl -
  • you need to implement the function computeNextIteration!( algo ) for your algo

History

This package grew out of the R package mopt.

Thanks to all Contributors!