321 lines
16 KiB
Matlab
321 lines
16 KiB
Matlab
function [ data, SS_out, error_flag] = solve_two_constraints(M_,dr, opts_simul_, solve_DM)
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% function [ data, SS_out, error_flag] = solve_two_constraints(M_,dr, opts_simul_, solve_DM)
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%
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% INPUT:
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% - M_ [structure] Matlab's structure describing the model (M_).
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% - dr [structure] decision rules for the model
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% - opts_simul [structure] Matlab's structure containing the Occbin options (opts_simul).
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% - solve_DM [double] indicator on whether to recompute decision rules
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%
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% OUTPUT:
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% - data [structure] simulation result containing fields:
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% - linear: paths for endogenous variables ignoring OBC (linear solution)
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% - piecewise: paths for endogenous variables satisfying the OBC (occbin/piecewise solution)
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% - ys: vector of steady state values
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% - regime_history: information on number and time of regime transitions
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% - SS_out [structure] State space solution
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% - T: [n_vars by n_vars by n_shock_period] array of transition matrices
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% - R: [n_vars by n_exo by n_shock_period] array of shock response matrices
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% - C: [n_vars by n_shock_period] array of constants
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% - error_flag [integer] 1 if a problem was encoutered, 0 otherwise
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% Original authors: Luca Guerrieri and Matteo Iacoviello
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% Original file downloaded from:
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% https://www.matteoiacoviello.com/research_files/occbin_20140630.zip
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% Adapted for Dynare by Dynare Team.
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%
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% This code is in the public domain and may be used freely.
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% However the authors would appreciate acknowledgement of the source by
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% citation of any of the following papers:
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%
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% Luca Guerrieri and Matteo Iacoviello (2015): "OccBin: A toolkit for solving
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% dynamic models with occasionally binding constraints easily"
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% Journal of Monetary Economics 70, 22-38
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persistent DM
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if isempty(DM)
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solve_DM=true;
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end
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data.shocks_sequence= opts_simul_.SHOCKS; % sequence of unforeseen shocks under which one wants to solve the model
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n_periods = opts_simul_.periods; % simulation horizon (can be longer than the sequence of shocks defined in shockssequence; must be long enough to ensure convergence back to the reference model at the end of the simulation horizon and may need to be varied depending on the sequence of shocks).
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curb_retrench = opts_simul_.curb_retrench; % 0: updates guess based on previous iteration; 1: updates similar to Gauss-Jacobi scheme, slowing iterations down by updating guess only one period at a time
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max_iter = opts_simul_.maxit; % maximum number of iterations allowed for the solution algorithm
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endo_init = opts_simul_.endo_init; % initial condition for state variables, in deviation from steady state in declaration order
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binding_indicator = opts_simul_.init_binding_indicator; % initial guess for constraint violations
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regime_history_guess = opts_simul_.init_regime; % initial guess for constraint violations
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periodic_solution = opts_simul_.periodic_solution;
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data.exo_pos = opts_simul_.exo_pos;
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n_shocks_periods = size(data.shocks_sequence,1);
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if n_periods < n_shocks_periods
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n_periods = n_shocks_periods;
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end
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nperiods_0 = max(opts_simul_.check_ahead_periods,n_periods-n_shocks_periods);
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error_flag=0;
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M00_ = M_;
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% ensure that all models have the same parameters
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% use the parameters for the base model.
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%keep the correct auxiliary regime specific parameter values
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data.ys = dr.ys;
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if solve_DM %recompute solution matrices
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[DM.Cbarmat ,DM.Bbarmat, DM.Abarmat, DM.Jbarmat] = occbin.get_deriv(M00_,data.ys);
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M10_ = M00_;
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M10_.params(strmatch(M_.occbin.constraint(1).pswitch,M00_.param_names,'exact'))= 1;
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[DM.Cbarmat10, DM.Bbarmat10, DM.Abarmat10, DM.Jbarmat10, DM.Dbarmat10] = occbin.get_deriv(M10_,data.ys);
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M01_ = M00_;
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M01_.params(strmatch(M_.occbin.constraint(2).pswitch,M00_.param_names,'exact'))= 1;
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[DM.Cbarmat01, DM.Bbarmat01, DM.Abarmat01, DM.Jbarmat01, DM.Dbarmat01] = occbin.get_deriv(M01_,data.ys);
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M11_ = M00_;
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M11_.params(M_.occbin.constraint(1).pswitch_index)= 1;
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M11_.params(M_.occbin.constraint(2).pswitch_index)= 1;
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[DM.Cbarmat11, DM.Bbarmat11, DM.Abarmat11, DM.Jbarmat11, DM.Dbarmat11] = occbin.get_deriv(M11_,data.ys);
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[DM.decrulea,DM.decruleb]=occbin.get_pq(dr);
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update_flag=true;
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DM.n_vars = M00_.endo_nbr;
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DM.n_exo = M00_.exo_nbr;
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else
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update_flag=false;
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end
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endo_names = M00_.endo_names;
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exo_names = M00_.exo_names;
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init_orig_ = endo_init;
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zdatapiecewise_ = zeros(n_periods,DM.n_vars);
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if ~exist('binding_indicator','var')
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binding_indicator = false(nperiods_0+1,2); % This sets the first guess for when
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% the constraints are going to hold.
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% The variable is a boolean with two columns. The first column refers to
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% constrain1_; the second to constrain2_.
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% Each row is a period in time.
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% If the boolean is true it indicates the relevant constraint is expected
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% to evaluate to true.
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% The default initial guess is consistent with the base model always
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% holding -- equivalent to the linear solution.
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else
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if size(binding_indicator,1)<(nperiods_0+1)
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binding_indicator = [binding_indicator; false(nperiods_0+1-size(binding_indicator,1),2)];
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end
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end
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SS_out.T = NaN(DM.n_vars,DM.n_vars,n_shocks_periods);
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SS_out.R = NaN(DM.n_vars,DM.n_exo,n_shocks_periods);
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SS_out.C = NaN(DM.n_vars,n_shocks_periods);
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if ~exist('regime_history_','var') || isempty(regime_history_guess)
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regime_history = struct();
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guess_history = false;
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else
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guess_history = true; %previous information exists
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regime_history = regime_history_guess;
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end
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if opts_simul_.waitbar
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hh = dyn_waitbar(0,'Occbin: Solving the model');
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set(hh,'Name','Occbin: Solving the model.');
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end
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for shock_period = 1:n_shocks_periods
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if opts_simul_.waitbar
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dyn_waitbar(shock_period/n_shocks_periods, hh, sprintf('Period %u of %u', shock_period,n_shocks_periods));
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end
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regime_change_this_iteration=true;
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iter = 0;
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guess_history_it = false;
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if guess_history && (shock_period<=length(regime_history_guess)) %beyond guess regime history
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guess_history_it = true;
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end
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is_periodic=false;
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binding_indicator_history={};
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max_err = NaN(max_iter,1);
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while (regime_change_this_iteration && iter<max_iter && ~is_periodic)
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iter = iter +1;
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if any(binding_indicator(end,:)) && nperiods_0<opts_simul_.max_periods
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binding_indicator = [binding_indicator; false(2,1)];
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nperiods_0 = nperiods_0 + 1;
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disp_verbose(['nperiods has been endogenously increased up to ' int2str(nperiods_0) '.'],opts_simul_.debug)
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end
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if size(binding_indicator,1)<(nperiods_0 + 1)
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binding_indicator=[binding_indicator; false(nperiods_0 + 1-size(binding_indicator,1),2)];
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end
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binding_indicator_history{iter}=binding_indicator;
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if iter==1 && guess_history_it
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regime_1 = regime_history_guess(shock_period).regime1;
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regime_start_1 = regime_history_guess(shock_period).regimestart1;
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binding_indicator(:,1) = regime_1(end);
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for ir=1:length(regime_1)-1
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binding_indicator(regime_start_1(ir):regime_start_1(ir+1)-1,1) = regime_1(ir);
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end
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regime_2 = regime_history_guess(shock_period).regime2;
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regime_start_2 = regime_history_guess(shock_period).regimestart2;
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binding_indicator(:,2) = regime_2(end);
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for ir=1:length(regime_2)-1
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binding_indicator(regime_start_2(ir):regime_start_2(ir+1)-1,2) = regime_2(ir);
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end
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nperiods_0 = size(binding_indicator,1)-1; %if history is present, update may be required
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end
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% analyse violvec and isolate contiguous periods in the other regime.
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[regime_1, regime_start_1, error_code_period(1)]=occbin.map_regime(binding_indicator(:,1),opts_simul_.debug);
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regime_history(shock_period).regime1 = regime_1;
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regime_history(shock_period).regimestart1 = regime_start_1;
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[regime_2, regime_start_2, error_code_period(2)]=occbin.map_regime(binding_indicator(:,2),opts_simul_.debug);
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regime_history(shock_period).regime2 = regime_2;
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regime_history(shock_period).regimestart2 = regime_start_2;
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if shock_period==1 || shock_period>1 && any(data.shocks_sequence(shock_period,:)) % first period or shock happening
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Tmax=max([regime_start_1(end) regime_start_2(end)])-1;
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[zdatalinear_, SS_out.T(:,:,shock_period), SS_out.R(:,:,shock_period), SS_out.C(:,shock_period), SS, update_flag]=occbin.mkdatap_anticipated_2constraints_dyn(nperiods_0,...
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DM,Tmax,...
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binding_indicator,...
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data.exo_pos,data.shocks_sequence(shock_period,:),endo_init, update_flag);
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[binding, relax, err]=feval([M_.fname,'.eval_difference'],zdatalinear_,M_,dr.ys);
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binding_constraint_new=[binding.constraint_1;binding.constraint_2];
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relaxed_constraint_new = [relax.constraint_1;relax.constraint_2];
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err_binding_constraint_new = [err.binding_constraint_1; err.binding_constraint_2];
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err_relaxed_constraint_new = [err.relax_constraint_1; err.relax_constraint_2];
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% check if changes_
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if any(binding_constraint_new & ~binding_indicator(:)) || any(relaxed_constraint_new & binding_indicator(:))
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err_violation = err_binding_constraint_new(binding_constraint_new & ~binding_indicator(:));
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err_relax = err_relaxed_constraint_new(relaxed_constraint_new & binding_indicator(:));
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max_err(iter) = max(abs([err_violation;err_relax]));
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regime_change_this_iteration = true;
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else
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regime_change_this_iteration = false;
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max_err(iter) = 0;
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end
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if curb_retrench % apply Gauss-Seidel idea of slowing down the change in the guess
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% for the constraint -- only relax one
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% period at a time starting from the last
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% one when each of the constraints is true.
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retrench = false(numel(binding_indicator),1);
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max_relax_constraint_1=find(relax.constraint_1 & binding_indicator(:,1),1,'last');
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if ~isempty(max_relax_constraint_1) && find(relax.constraint_1,1,'last')>=find(binding_indicator(:,1),1,'last')
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retrench(max_relax_constraint_1) = true;
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end
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max_relax_constraint_2=find(relax.constraint_2 & binding_indicator(:,2),1,'last');
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if ~isempty(max_relax_constraint_2) && find(relax.constraint_2,1,'last')>=find(binding_indicator(:,2),1,'last')
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retrench(max_relax_constraint_2+nperiods_0+1) = true;
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end
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binding_indicator = (binding_indicator(:) | binding_constraint_new) & ~ retrench;
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else
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binding_indicator= (binding_indicator(:) | binding_constraint_new) & ~(binding_indicator(:) & relaxed_constraint_new);
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end
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binding_indicator = reshape(binding_indicator,nperiods_0+1,2);
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if iter>1 && regime_change_this_iteration
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is_periodic=false(1,iter-1);
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for kiter=1:iter-1
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vvv = [binding_indicator_history{kiter}; false(size(binding_indicator,1)- size(binding_indicator_history{kiter},1), 2)];
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is_periodic(kiter) = isequal(vvv, binding_indicator);
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end
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is_periodic_all = is_periodic;
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is_periodic = any(is_periodic);
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if is_periodic && periodic_solution
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[min_err,index_min_err]=min(max_err(find(is_periodic_all,1):end));
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inx = find(is_periodic_all,1):iter;
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inx = inx(index_min_err);
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binding_indicator=binding_indicator_history{inx}; %select regime history with same result, but smallest error
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if inx<iter %update if needed
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[regime_1, regime_start_1, error_code_period(1)]=occbin.map_regime(binding_indicator(:,1),opts_simul_.debug);
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regime_history(shock_period).regime1 = regime_1;
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regime_history(shock_period).regimestart1 = regime_start_1;
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[regime_2, regime_start_2, error_code_period(2)]=occbin.map_regime(binding_indicator(:,2),opts_simul_.debug);
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regime_history(shock_period).regime2 = regime_2;
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regime_history(shock_period).regimestart2 = regime_start_2;
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Tmax=max([regime_start_1(end) regime_start_2(end)])-1;
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% get the hypothesized piece wise linear solution
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[zdatalinear_, SS_out.T(:,:,shock_period), SS_out.R(:,:,shock_period), SS_out.C(:,shock_period), SS, update_flag]=occbin.mkdatap_anticipated_2constraints_dyn(nperiods_0,DM,...
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Tmax,...
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binding_indicator,...
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data.exo_pos,data.shocks_sequence(shock_period,:),endo_init,update_flag);
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end
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end
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end
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else
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regime_change_this_iteration= false;
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zdatalinear_(1:end-1,:)=zdatalinear_(2:end,:);
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zdatalinear_(end,:) = DM.decrulea*zdatalinear_(end-1,:)';
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if length(SS)>1
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SS=SS(2:end);
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else
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SS=[];
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end
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if isempty(SS)
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SS_out.T(:,:,shock_period)= DM.decrulea;
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SS_out.R(:,:,shock_period)= DM.decruleb;
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SS_out.C(:,shock_period)= 0;
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else
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SS_out.T(:,:,shock_period)= SS(1).T;
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SS_out.R(:,:,shock_period)= SS(1).R;
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SS_out.C(:,shock_period)= SS(1).C;
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end
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binding_indicator_history{iter}=binding_indicator;
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end
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end
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if regime_change_this_iteration && max_iter>1
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disp_verbose(['occbin solver: period ' int2str(shock_period) ':'],opts_simul_.debug)
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if is_periodic
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disp_verbose('Occbin solver loops between two regimes.',opts_simul_.debug)
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if periodic_solution
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disp_verbose(['Max error:' num2str(min_err) '.'],opts_simul_.debug)
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else
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error_flag = 310;
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if opts_simul_.waitbar; dyn_waitbar_close(hh); end
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return;
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end
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else
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disp_verbose('Did not converge -- increase maxit.',opts_simul_.debug)
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error_flag = 311;
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if opts_simul_.waitbar; dyn_waitbar_close(hh); end
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return;
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end
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end
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if any(error_code_period)
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disp_verbose('Increase nperiods.',opts_simul_.debug)
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error_flag = 312;
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if opts_simul_.waitbar; dyn_waitbar_close(hh); end
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return;
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end
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endo_init = zdatalinear_(1,:);
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zdatapiecewise_(shock_period,:)=endo_init;
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endo_init= endo_init';
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% update the guess for constraint violations for next period
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% update is consistent with expecting no additional shocks next period
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binding_indicator=[binding_indicator(2:end,:); false(1,2)];
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end
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zdatapiecewise_(shock_period+1:end,:)=zdatalinear_(2:n_periods-shock_period+1,:);
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data.piecewise=zdatapiecewise_;
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data.regime_history=regime_history;
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if ~opts_simul_.piecewise_only
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% get the linear responses
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data.linear = occbin.mkdata(n_periods,DM.decrulea,DM.decruleb,endo_names,exo_names,[],data.exo_pos,data.shocks_sequence,init_orig_);
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end
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if opts_simul_.waitbar
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dyn_waitbar_close(hh);
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end |