135 lines
4.0 KiB
Julia
135 lines
4.0 KiB
Julia
module PerfectForesightModelSolver
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##
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# Copyright (C) 2016 Dynare Team
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#
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# This file is part of Dynare.
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#
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# Dynare is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# Dynare is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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##
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import DynareModel.Model
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import DynareOutput.Output
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import DynareOptions.Options
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export simulate_perfect_foresight_model!
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function simulate_perfect_foresight_model!(endogenousvariables::Matrix{Float64}, exogenousvariables::Matrix{Float64}, steadystate::Vector{Float64}, model::Model, options::Options)
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lead_lag_incidence = model.lead_lag_incidence
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nyp = countnz(lead_lag_incidence[1,:])
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ny0 = countnz(lead_lag_incidence[2,:])
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nyf = countnz(lead_lag_incidence[3,:])
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ny = length(model.endo)
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nd = nyp+ny0+nyf
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periods = options.pfmsolver.periods
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params = model.params
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tmp = lead_lag_incidence[2:3,:]'
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i_cols_A1 = find(tmp)
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i_cols_1 = tmp[i_cols_A1]
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tmp = lead_lag_incidence[1:2,:]'
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i_cols_AT = find(tmp)
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i_cols_T = tmp[i_cols_AT]
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tmp = lead_lag_incidence[2,:]'
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i_cols_A0 = find(tmp)
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i_cols_0 = tmp[i_cols_A0]
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i_cols_j = collect(1:nd)
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i_upd = ny+collect(1:periods*ny)
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Y = vec(endogenousvariables)
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z = Y[find(lead_lag_incidence')]
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jacobian = zeros(Float64, ny, length(z)+length(model.exo))
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residuals = zeros(Float64, ny)
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println("\nMODEL SIMULATION:\n")
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rd = zeros(Float64, periods*ny)
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iA = zeros(Int64, periods*model.nnzderivatives[1])
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jA = zeros(Int64, periods*model.nnzderivatives[1])
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vA = zeros(Float64, periods*model.nnzderivatives[1])
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convergence = false
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iteration = 0
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while !convergence
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iteration += 1
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i_rows = collect(1:ny)
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i_cols_A = find(lead_lag_incidence')
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i_cols = i_cols_A
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m = 0
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for it = 2:(periods+1)
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model.dynamic(Y[i_cols], exogenousvariables, params, steadystate, it, residuals, jacobian)
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if it==(periods+1) & it==2
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(r, c, v) = findnz(jacobian[:,i_cols_0])
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k = collect(1:length(v))+m
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iA[k] = i_rows[r]
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jA[k] = i_cols_A0[c]
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vA[k] = v
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elseif it==(periods+1)
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(r, c, v) = findnz(jacobian[:,i_cols_T])
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k = collect(1:length(v))+m
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iA[k] = i_rows[r]
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jA[k] = i_cols_A[i_cols_T[c]]
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vA[k] = v
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elseif it==2
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(r, c, v) = findnz(jacobian[:,i_cols_1])
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k = collect(1:length(v))+m
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iA[k] = i_rows[r]
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jA[k] = i_cols_A1[c]
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vA[k] = v
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else
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(r, c, v) = findnz(jacobian[:,i_cols_j])
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k = collect(1:length(v))+m
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iA[k] = i_rows[r]
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jA[k] = i_cols_A[c]
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vA[k] = v
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end
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m += length(v)
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rd[i_rows] = residuals
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i_rows += ny
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i_cols += ny
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if it>2
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i_cols_A += ny
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end
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end
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err = maximum(abs(rd))
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println("Iter. ", iteration, "\t err. ", round(err, 12))
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if err<options.pfmsolver.tolf
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iteration -= 1
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convergence = true
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end
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A = sparse(iA[1:m], jA[1:m], vA[1:m])
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dy = -A\rd
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Y[i_upd] += dy
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if maximum(abs(dy))<options.pfmsolver.tolx
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convergence = true
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end
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end
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if convergence
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println("\nPFM solver converged in ", iteration, " iterations!\n")
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endogenousvariables = reshape(Y, ny, periods+2)
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end
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end
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end
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