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Fixes for julia 0.6.x. 

Updated the preprocessor module (adds new static and dynamic routines which
only return the jacobian).
time-shift
Stéphane Adjemian(Charybdis) 2018-03-10 15:09:36 +01:00
parent 909956f0bc
commit d96aca093e
9 changed files with 124 additions and 114 deletions
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56
julia/DynareModel.jl
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@ -1,6 +1,6 @@
module DynareModel
##
# Copyright (C) 2015 Dynare Team
# Copyright (C) 2015-2018 Dynare Team
#
# This file is part of Dynare.
#
@ -20,7 +20,7 @@ module DynareModel
export Model, Endo, Exo, ExoDet, Param, dynare_model
abstract Atom
abstract type Atom end
immutable Endo <: Atom
name::String
@ -88,6 +88,7 @@ type Model
aux_vars::Vector{AuxVars}
pred_vars::Vector{Int}
obs_vars::Vector{Int}
state_var::Vector{Int}
orig_endo_nbr::Int
orig_eq_nbr::Int
eq_nbr::Int
@ -106,6 +107,14 @@ type Model
maximum_endo_lead::Int
maximum_exo_lag::Int
maximum_exo_lead::Int
orig_maximum_lag::Int
orig_maximum_lead::Int
orig_maximum_endo_lag::Int
orig_maximum_endo_lead::Int
orig_maximum_exo_lag::Int
orig_maximum_exo_lead::Int
orig_maximum_exo_det_lag::Int
orig_maximum_exo_det_lead::Int
lead_lag_incidence::Matrix{Int}
nnzderivatives::Vector{Int}
analytical_steady_state::Bool
@ -130,18 +139,19 @@ function dynare_model()
return Model("", # fname
"", # dname
"", # dynare_version
Array(Endo,0), # endo
Array(Exo,0), # exo
Array(ExoDet,0), # exo_det
Array(Param,0), # param
Array(AuxVars,0), # aux_vars
Array(Int,0), # pred_vars
Array(Int,0), # obs_vars
Vector{Endo}(), # endo
Vector{Exo}(), # exo
Vector{ExoDet}(), # exo_det
Vector{Param}(), # param
Vector{AuxVars}(), # aux_vars
Vector{Int}(), # pred_vars
Vector{Int}(), # obs_vars
Vector{Int}(), # state_var
0, # orig_endo_nbr
0, # orig_eq_nbr
0, # eq_nbr
0, # ramsey_eq_nbr
Array(DetShocks,0), # det_shocks
Vector{DetShocks}(), # det_shocks
0, # nstatic
0, # nfwrd
0, # npred
@ -155,19 +165,27 @@ function dynare_model()
0, # maximum_endo_lead
0, # maximum_exo_lag
0, # maximum_exo_lead
Array(Int, 3, 0), # lead_lag_incidence
zeros(Int, 3), # nnzderivatives
0, # orig_maximum_lag
0, # orig_maximum_lead
0, # orig_maximum_endo_lag
0, # orig_maximum_endo_lead
0, # orig_maximum_exo_lag
0, # orig_maximum_exo_lead
0, # orig_maximum_exo_det_lag
0, # orig_maximum_exo_det_lead
Matrix{Int}(0,0), # lead_lag_incidence
zeros(Int64,3), # nnzderivatives
false, # analytical_steady_state
false, # user_written_analytical_steady_state
false, # static_and_dynamic_models_differ
Array(String,0), # equation_tags
Array(Int64,1), # exo_names_orig_ord
Array(Float64, 0, 0), # sigma_e (Cov matrix of the structural innovations)
Array(Float64, 0, 0), # correlation_matrix (Corr matrix of the structural innovations)
Array(Float64, 0, 0), # h (Cov matrix of the measurement errors)
Array(Float64, 0, 0), # correlation_matrix_me (Cov matrix of the measurement errors)
Vector{String}(), # equation_tags
Vector{Int}(), # exo_names_orig_ord
Matrix{Float64}(0,0), # sigma_e (Cov matrix of the structural innovations)
Matrix{Float64}(0,0), # correlation_matrix (Corr matrix of the structural innovations)
Matrix{Float64}(0,0), # h (Cov matrix of the measurement errors)
Matrix{Float64}(0,0), # correlation_matrix_me (Cov matrix of the measurement errors)
true, # sigma_e_is_diagonal
Array(Float64, 0), # params
Vector{Float64}(), # params
function()end, # static
function()end, # static_params_derivs
function()end, # dynamic

6
julia/DynareOutput.jl
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@ -1,6 +1,6 @@
module DynareOutput
##
# Copyright (C) 2015 Dynare Team
# Copyright (C) 2015-2018 Dynare Team
#
# This file is part of Dynare.
#
@ -28,8 +28,8 @@ end
function dynare_output()
return Output("", # dynare_version
Array(Float64, 0), # steady_state
Array(Float64, 0) # exo_steady_state
Vector{Float64}(), # steady_state
Vector{Float64}() # exo_steady_state
)
end

4
julia/PerfectForesightModelSolver.jl
View File

@ -112,7 +112,7 @@ function simulate_perfect_foresight_model!(endogenousvariables::Matrix{Float64},
i_cols_A += ny
end
end
err = maximum(abs(rd))
err = maximum(abs.(rd))
println("Iter. ", iteration, "\t err. ", round(err, 12))
if err<options.pfmsolver.tolf
iteration -= 1
@ -121,7 +121,7 @@ function simulate_perfect_foresight_model!(endogenousvariables::Matrix{Float64},
A = sparse(iA[1:m], jA[1:m], vA[1:m])
dy = -A\rd
Y[i_upd] += dy
if maximum(abs(dy))<options.pfmsolver.tolx
if maximum(abs.(dy))<options.pfmsolver.tolx
convergence = true
end
end

16
julia/SteadyState.jl
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@ -47,22 +47,26 @@ function steady!(model::Model, oo::Output)
end
function steady(model::Model, oo::Output, yinit::Vector{Float64})
ojectivefunction!(y::Vector{Float64}, fval::Vector{Float64}, fjac::Array{Float64}) = model.static(y, oo.exo_steady_state, model.params, fval, fjac)
r = nlsolve(only_fg!(ojectivefunction!), yinit, show_trace=false)
f!(fval::Vector{Float64}, y::Vector{Float64}) = model.static(y, oo.exo_steady_state, model.params, fval)
j!(fjac::Matrix{Float64}, y::Vector{Float64}) = model.static(y, oo.exo_steady_state, model.params, fjac)
fj!(fval::Vector{Float64}, fjac::Matrix{Float64}, y::Vector{Float64}) = model.static(y, oo.exo_steady_state, model.params, fval, fjac)
r = nlsolve(f!, j!, fj!, yinit, show_trace=false)
if converged(r)
return r.zero
else
return fill(nan(Float64), length(yinit))
return fill(NaN, length(yinit))
end
end
function steady!(model::Model, oo::Output, yinit::Vector{Float64})
ojectivefunction!(y::Vector{Float64}, fval::Vector{Float64}, fjac::Array{Float64}) = model.static(y, oo.exo_steady_state, model.params, fval, fjac)
r = nlsolve(only_fg!(ojectivefunction!), yinit, show_trace=false)
f!(fval::Vector{Float64}, y::Vector{Float64}) = model.static(y, oo.exo_steady_state, model.params, fval)
j!(fjac::Matrix{Float64}, y::Vector{Float64}) = model.static(y, oo.exo_steady_state, model.params, fjac)
fj!(fval::Vector{Float64}, fjac::Matrix{Float64}, y::Vector{Float64}) = model.static(y, oo.exo_steady_state, model.params, fval, fjac)
r = nlsolve(f!, j!, fj!, yinit, show_trace=false)
if converged(r)
oo.steady_state = r.zero
else
oo.steady_state = fill(nan(Float64), length(yinit))
oo.steady_state = fill(NaN, length(yinit))
end
end

2
preprocessor

@ -1 +1 @@
Subproject commit 7a4312c48bf6a1e05ecd7d3c04075ed3017b37e4
Subproject commit 84c6bf5daf051779e4620ad9f7a2c2aa52b623f5

90
tests/julia/rbc/test1.jl
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@ -3,90 +3,78 @@ if isempty(findin([abspath("../../../julia")], LOAD_PATH))
unshift!(LOAD_PATH, abspath("../../../julia"))
end
# Load Dynare package
importall Dynare
using Dynare
using SteadyState
# Compile the rbc.mod file -> produce a module with the model definition.
@dynare "rbc1.mod"
importall SteadyState
# First call to the steady state routine (analytical)
@time SteadyState.steady!(model_, oo_)
# First call to the steady state routine (analytical)
@time SteadyState.steady!(model_, oo_)
paramsinit = copy(model_.params);
steadyState = deepcopy(oo_.steady_state)
yinit = deepcopy(oo_.steady_state)
y_init = copy(yinit)
y_init[1] = 1.1*yinit[1]
y_init[2] = 0.9*yinit[2]
y_init = copy(steadyState)
y_init[1] = 1.1*steadyState[1]
y_init[2] = 0.9*steadyState[2]
# First call to the steady state routine (numerical)
println("First call to the numerical steady state routine")
@time SteadyState.steady!(model_, oo_, yinit)
@time SteadyState.steady!(model_, oo_, y_init)
# Check results
@assert maximum(abs(oo_.steady_state-yinit))<1e-9
@assert maximum(abs.(oo_.steady_state-steadyState))<1e-6
y_init = copy(yinit)
yinit[1] = 1.1*yinit[1]
yinit[2] = 0.9*yinit[2]
yinit = deepcopy(steadyState)
yinit[1] = 1.1*steadyState[1]
yinit[2] = 0.9*steadyState[2]
# Second call to the steady state routine (numerical)
println("Second call to the numerical steady state routine")
@time SteadyState.steady!(model_, oo_, yinit)
params = model_.params
# change alpha
println("Change α")
model_.params[4] = max(min(1.0, model_.params[4]*1.2), 0.0)
ys = SteadyState.steady(model_, oo_)
y_init = copy(yinit)
@time SteadyState.steady!(model_, oo_, y_init)
y_init = copy(yinit)
@time SteadyState.steady!(model_, oo_, y_init)
@assert maximum(abs(oo_.steady_state-ys))<1e-9
model_.params = deepcopy(params)
model_.params[4] = max(min(1.0, params[4]*1.1), 0.0)
@time ys = SteadyState.steady(model_, oo_) # Analytical steady state
@time SteadyState.steady!(model_, oo_, steadyState)
@assert maximum(abs.(oo_.steady_state-ys))<1e-6
# change delta
println("Change δ")
model_.params[6] = max(min(1.0, model_.params[6]*1.2), 0.0)
ys = SteadyState.steady(model_, oo_)
y_init = copy(yinit)
@time SteadyState.steady!(model_, oo_, y_init)
y_init = copy(yinit)
@time SteadyState.steady!(model_, oo_, y_init)
@assert maximum(abs(oo_.steady_state-ys))<1e-9
model_.params = deepcopy(params)
model_.params[6] = max(min(1.0, params[6]*1.1), 0.0)
@time ys = SteadyState.steady(model_, oo_) # Analytical steady state
@time SteadyState.steady!(model_, oo_, steadyState)
@assert maximum(abs.(oo_.steady_state-ys))<1e-6
# change beta
println("Change β")
model_.params[1] = max(min(1-1e-6, model_.params[1]*0.99), 0.0)
ys = SteadyState.steady(model_, oo_)
y_init = copy(yinit)
@time SteadyState.steady!(model_, oo_, y_init)
y_init = copy(yinit)
@time SteadyState.steady!(model_, oo_, y_init)
@assert maximum(abs(oo_.steady_state-ys))<1e-9
model_.params = deepcopy(params)
model_.params[1] = max(min(1-1e-6, params[1]*0.99), 0.0)
@time ys = SteadyState.steady(model_, oo_) # Analytical steady state
@time SteadyState.steady!(model_, oo_, steadyState)
@assert maximum(abs.(oo_.steady_state-ys))<1e-6
# change tau
println("Change τ")
model_.params[3] /= 1.5
ys = SteadyState.steady(model_, oo_)
y_init = copy(yinit)
@time SteadyState.steady!(model_, oo_, y_init)
y_init = copy(yinit)
@time SteadyState.steady!(model_, oo_, y_init)
@assert maximum(abs(oo_.steady_state-ys))<1e-9
model_.params = deepcopy(params)
model_.params[3] = params[3]/1.1
@time ys = SteadyState.steady(model_, oo_)
@time SteadyState.steady!(model_, oo_, steadyState)
@assert maximum(abs.(oo_.steady_state-ys))<1e-6
# change Epsilon
println("Change ϵ")
model_.params[5] *= 1.5
ys = SteadyState.steady(model_, oo_)
y_init = copy(yinit)
@time SteadyState.steady!(model_, oo_, y_init)
y_init = copy(yinit)
@time SteadyState.steady!(model_, oo_, y_init)
@assert maximum(abs(oo_.steady_state-ys))<1e-9
model_.params = deepcopy(params)
model_.params[5] = params[5]*1.1
@time ys = SteadyState.steady(model_, oo_)
@time SteadyState.steady!(model_, oo_, steadyState)
@assert maximum(abs.(oo_.steady_state-ys))<1e-6

49
tests/julia/rbc/test1pfm.jl
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@ -3,18 +3,20 @@ if isempty(findin([abspath("../../../julia")], LOAD_PATH))
unshift!(LOAD_PATH, abspath("../../../julia"))
end
# Load Dynare package
importall Dynare
using PyPlot
# Load packages
using Plots
using Dynare
using SteadyState
using PerfectForesightModelSolver
plotlyjs() # Choose a backend
# Compile the rbc.mod file -> produce a module with the model definition.
@dynare "rbc1.mod"
importall SteadyState
importall PerfectForesightModelSolver
# First call to the steady state routine (analytical)
@time SteadyState.steady!(model_, oo_)
println(oo_.steady_state)
@ -32,10 +34,9 @@ exogenousvariables = repmat(oo_.exo_steady_state', options_.pfmsolver.periods+2,
n = 200
dates = collect(0:n-1)
plt[:figure](1)
#plt[:figure](1)
plot(dates, vec(endogenousvariables[1,1:n]), color="black", linewidth=2.0, linestyle="-")
# Initialize paths for the endogenous variables
endogenousvariables = repmat(oo_.steady_state, 1, options_.pfmsolver.periods+2)
# Destroy part of the initial stock of physical capital...
@ -65,19 +66,19 @@ exogenousvariables[10+1, 1] = 2
# Simulate the paths for the endogenous variables, given the expected shock
@time simulate_perfect_foresight_model!(endogenousvariables, exogenousvariables, oo_.steady_state, model_, options_)
n = 200
dates = collect(0:n-1)
plt[:figure](2)
subplot(221)
title("Efficiency")
plot(dates, vec(endogenousvariables[5,1:n]), color="black", linewidth=2.0, linestyle="-")
subplot(223)
title("Output")
plot(dates, vec(endogenousvariables[2,1:n]), color="black", linewidth=2.0, linestyle="-")
subplot(222)
title("Consumption")
plot(dates, vec(endogenousvariables[4,1:n]), color="black", linewidth=2.0, linestyle="-")
subplot(224)
title("Labour")
plot(dates, vec(endogenousvariables[3,1:n]), color="black", linewidth=2.0, linestyle="-")
suptitle("Expected positive expected shock")
# dates = collect(0:n-1)
# n = 200
# plt[:figure](2)
# subplot(221)
# title("Efficiency")
# plot(dates, vec(endogenousvariables[5,1:n]), color="black", linewidth=2.0, linestyle="-")
# subplot(223)
# title("Output")
# plot(dates, vec(endogenousvariables[2,1:n]), color="black", linewidth=2.0, linestyle="-")
# subplot(222)
# title("Consumption")
# plot(dates, vec(endogenousvariables[4,1:n]), color="black", linewidth=2.0, linestyle="-")
# subplot(224)
# title("Labour")
# plot(dates, vec(endogenousvariables[3,1:n]), color="black", linewidth=2.0, linestyle="-")
# suptitle("Expected positive expected shock")

14
tests/julia/rbc/test2.jl
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@ -3,14 +3,12 @@ if isempty(findin([abspath("../../../julia")], LOAD_PATH))
unshift!(LOAD_PATH, abspath("../../../julia"))
end
# Load Dynare package
importall Dynare
# Load Dynare package(s)
using Dynare
using SteadyState
# Compile the rbc.mod file -> produce a module with the model definition.
@dynare "rbc1.mod"
@dynare "rbc2.mod"
importall SteadyState
steady!(model_, oo_)
# Compute the steady state
steady_state!(model_, oo_)

1
tests/julia/rbc/test3.jl
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@ -17,4 +17,5 @@ importall Dynare
# using rbc1
print(rbc1.model_.fname)
print(rbc2.model_.fname)