MomentOpt.jl
Simulated Method of Moments for Julia
A package providing supporting infrastructure and algorithms to perform Simulated Method of Moments.
Features
- Supply your own objective function to be optimized
- Optimization can be carried out in parallel
- Diagnostic tools illustrating the proposal distribution as well as markov chain statistics.
- Includes a parellel tempering likelihood-free optimizer using MCMC technology and a coordinate descent optimizer.
For some example usage see the Examples page.
Manual Outline
MProb: Minimisation/Maximisation Problems
- A core object of this library is the
MProbtype, specifying an optimisation problem.
MomentOpt.MProb — Type.Minimisation Problem: MProb
A moment minimsation problem is defined by an objective function that depends on a vector of unknown parameters params_to_sample, and a set of datamoments moments. The key idea here is the one of simulated method of moments, where we use params_to_sample to simulate a model, some moments of which will be compared to moments from the data.
Fields:
initial_value: initial parameter value as a dictparams_to_sample:OrderedDictwith lower and upper boundsobjfunc: objective functionobjfunc_opts: options passed to the objective function, e.g. printlevelmoments: a dictionary or dataframe of data moments to track
Example:
pb = Dict(p1" => [0.2,-2,2] , "p2" => [-0.2,-2,2] )
moms = DataFrame(name=["mu2","mu1"],value=[0.0,0.0],weight=rand(2))
m = MProb()
addSampledParam!(m,pb)
addMoment!(m,moms)
MomentOpt.addEvalFunc!(m,MomentOpt.objfunc_norm)MomentOpt.addMoment! — Method.addMoment!(m::MProb,name::String,value,weight)MomentOpt.addMoment! — Method.addMoment!(m::MProb,name::String,value)MomentOpt.addMoment! — Method.addMoment!(m::MProb,name::Symbol,value,weight)Add Moments to an MProb. Adds a single moment to the mprob.
name: the name of the moment as a Symbol value: value of the moment weight: weight in the objective function
MomentOpt.addMoment! — Method.addMoment!(m::MProb,name::Symbol,value)MomentOpt.addParam! — Method.Add initial parameter values to an MProb minimisation problem.
MomentOpt.addParam! — Method.Add initial parameter values to an MProb minimisation problem.
Arguments:
p: A Dict with (String,Number) pairs
MomentOpt.addSampledParam! — Function.Add parameters to be sampled to an MProb.
d: a Dict with a triple (init,lb,ub) as value for each key.
MomentOpt.addSampledParam! — Method.Add parameters to be sampled to an MProb.
MomentOpt.evaluateObjective — Method.MomentOpt.evaluateObjective — Method.evaluateObjective(m::MProb,p::Union{Dict,OrderedDict};noseed=false)Evaluate the objective function of an MProb at a given parameter vector p. Set noseed to true if you want to generate new random draws for shocks in the objective function (necessary for estimation of standard errors in get_stdErrors, for example)
MomentOpt.mapto_01 — Method.mapto_01(p::OrderedDict,lb::Vector{Float64},ub::Vector{Float64})map param to [0,1]
MomentOpt.mapto_ab — Method.mapto_ab(p::Vector{Float64},lb::Vector{Float64},ub::Vector{Float64})map param from [0,1] to [a,b]
MomentOpt.ms_names — Method.Get the name of moments
MomentOpt.ps2s_names — Method.Get sampled parameter names from the MProb
MomentOpt.ps_names — Method.Get all parameter names from the MProb