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new implementation for extended path

time-shift
Michel Juillard 2015-02-06 12:36:09 +01:00
parent 085ab360cc
commit 9c6e219990
2 changed files with 162 additions and 87 deletions
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243
matlab/ep/extended_path.m
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@ -1,4 +1,4 @@
function ts = extended_path(initial_conditions,sample_size) function [ts,results] = extended_path(initial_conditions,sample_size)
% Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time % Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
% series of size T is obtained by solving T perfect foresight models. % series of size T is obtained by solving T perfect foresight models.
% %
@ -32,24 +32,28 @@ function ts = extended_path(initial_conditions,sample_size)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>. % along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global M_ options_ oo_ global M_ options_ oo_
options_.verbosity = options_.ep.verbosity; ep = options_.ep;
verbosity = options_.ep.verbosity+options_.ep.debug; options_.verbosity = ep.verbosity;
verbosity = ep.verbosity+ep.debug;
% Set maximum number of iterations for the deterministic solver. % Set maximum number of iterations for the deterministic solver.
options_.simul.maxit = options_.ep.maxit; options_.simul.maxit = ep.maxit;
% Prepare a structure needed by the matlab implementation of the perfect foresight model solver % Prepare a structure needed by the matlab implementation of the perfect foresight model solver
pfm = setup_stochastic_perfect_foresight_model_solver(M_,options_,oo_); pfm = setup_stochastic_perfect_foresight_model_solver(M_,options_,oo_);
endo_nbr = M_.endo_nbr;
exo_nbr = M_.exo_nbr; exo_nbr = M_.exo_nbr;
ep = options_.ep; maximum_lag = M_.maximum_lag;
maximum_lead = M_.maximum_lead;
epreplic_nbr = ep.replic_nbr;
steady_state = oo_.steady_state; steady_state = oo_.steady_state;
dynatol = options_.dynatol; dynatol = options_.dynatol;
% Set default initial conditions. % Set default initial conditions.
if isempty(initial_conditions) if isempty(initial_conditions)
if isempty(M_.endo_histval) if isempty(M_.endo_histval)
initial_conditions = oo_.steady_state; initial_conditions = steady_state;
else else
initial_conditions = M_.endo_histval; initial_conditions = M_.endo_histval;
end end
@ -57,18 +61,19 @@ end
% Set the number of periods for the perfect foresight model % Set the number of periods for the perfect foresight model
periods = options_.ep.periods; periods = ep.periods;
pfm.periods = periods; pfm.periods = periods;
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny); pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
pfm.block = options_.block;
% keep a copy of pfm.i_upd % keep a copy of pfm.i_upd
i_upd = pfm.i_upd; i_upd = pfm.i_upd;
% Set the algorithm for the perfect foresight solver % Set the algorithm for the perfect foresight solver
options_.stack_solve_algo = options_.ep.stack_solve_algo; options_.stack_solve_algo = ep.stack_solve_algo;
% Set check_stability flag % Set check_stability flag
do_not_check_stability_flag = ~options_.ep.check_stability; do_not_check_stability_flag = ~ep.check_stability;
% Compute the first order reduced form if needed. % Compute the first order reduced form if needed.
% %
@ -76,7 +81,7 @@ do_not_check_stability_flag = ~options_.ep.check_stability;
% all the globals in a mat file called linear_reduced_form.mat; % all the globals in a mat file called linear_reduced_form.mat;
dr = struct(); dr = struct();
if options_.ep.init if ep.init
options_.order = 1; options_.order = 1;
[dr,Info,M_,options_,oo_] = resol(1,M_,options_,oo_); [dr,Info,M_,options_,oo_] = resol(1,M_,options_,oo_);
end end
@ -85,12 +90,10 @@ end
options_.minimal_solving_period = 100;%options_.ep.periods; options_.minimal_solving_period = 100;%options_.ep.periods;
% Initialize the exogenous variables. % Initialize the exogenous variables.
% !!!!!!!! Needs to fixed
options_.periods = periods;
make_ex_; make_ex_;
% Initialize the endogenous variables. % Initialize the endogenous variables.
make_y_; %make_y_;
% Initialize the output array. % Initialize the output array.
time_series = zeros(M_.endo_nbr,sample_size); time_series = zeros(M_.endo_nbr,sample_size);
@ -107,19 +110,36 @@ covariance_matrix_upper_cholesky = chol(covariance_matrix);
%exo_nbr = effective_number_of_shocks; %exo_nbr = effective_number_of_shocks;
% Set seed. % Set seed.
if options_.ep.set_dynare_seed_to_default if ep.set_dynare_seed_to_default
set_dynare_seed('default'); set_dynare_seed('default');
end end
% Set bytecode flag % Set bytecode flag
bytecode_flag = options_.ep.use_bytecode; bytecode_flag = ep.use_bytecode;
% Set number of replications
replic_nbr = ep.replic_nbr;
% Simulate shocks. % Simulate shocks.
switch options_.ep.innovation_distribution switch ep.innovation_distribution
case 'gaussian' case 'gaussian'
oo_.ep.shocks = transpose(transpose(covariance_matrix_upper_cholesky)*randn(effective_number_of_shocks,sample_size)); shocks = transpose(transpose(covariance_matrix_upper_cholesky)* ...
randn(effective_number_of_shocks,sample_size*replic_nbr));
case 'calibrated'
replic_nbr = 1;
shocks = zeros(sample_size,effective_number_of_shocks);
for i = 1:length(M_.unanticipated_det_shocks)
k = M_.unanticipated_det_shocks(i).periods;
ivar = M_.unanticipated_det_shocks(i).exo_id;
v = M_.unanticipated_det_shocks(i).value;
if ~M_.unanticipated_det_shocks(i).multiplicative
shocks(k,ivar) = v;
else
socks(k,ivar) = shocks(k,ivar) * v;
end
end
shocks = shocks(:,positive_var_indx);
otherwise otherwise
error(['extended_path:: ' options_.ep.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!']) error(['extended_path:: ' ep.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
end end
@ -128,7 +148,7 @@ hh = dyn_waitbar(0,'Please wait. Extended Path simulations...');
set(hh,'Name','EP simulations.'); set(hh,'Name','EP simulations.');
% hybrid correction % hybrid correction
pfm.hybrid_order = options_.ep.stochastic.hybrid_order; pfm.hybrid_order = ep.stochastic.hybrid_order;
if pfm.hybrid_order if pfm.hybrid_order
oo_.dr = set_state_space(oo_.dr,M_,options_); oo_.dr = set_state_space(oo_.dr,M_,options_);
options = options_; options = options_;
@ -142,16 +162,16 @@ end
pfm.nnzA = M_.NNZDerivatives(1); pfm.nnzA = M_.NNZDerivatives(1);
% setting up integration nodes if order > 0 % setting up integration nodes if order > 0
if options_.ep.stochastic.order > 0 if ep.stochastic.order > 0
[nodes,weights,nnodes] = setup_integration_nodes(options_.ep,pfm); [nodes,weights,nnodes] = setup_integration_nodes(options_.ep,pfm);
pfm.nodes = nodes; pfm.nodes = nodes;
pfm.weights = weights; pfm.weights = weights;
pfm.nnodes = nnodes; pfm.nnodes = nnodes;
% compute number of blocks % compute number of blocks
[block_nbr,pfm.world_nbr] = get_block_world_nbr(options_.ep.stochastic.algo,nnodes,options_.ep.stochastic.order,options_.ep.periods); [block_nbr,pfm.world_nbr] = get_block_world_nbr(ep.stochastic.algo,nnodes,ep.stochastic.order,ep.periods);
else else
block_nbr = options_.ep.periods block_nbr = ep.periods;
end end
@ -167,74 +187,54 @@ oo_.ep.failures.previous_period = cell(0);
oo_.ep.failures.shocks = cell(0); oo_.ep.failures.shocks = cell(0);
% Initializes some variables. % Initializes some variables.
t = 0; t = 1;
tsimul = 1; tsimul = 1;
nx = length(oo_.exo_simul);
for k = 1:replic_nbr
results{k} = zeros(endo_nbr,sample_size+1);
results{k}(:,1) = initial_conditions;
end
% Main loop. % Main loop.
while (t<sample_size) while (t <= sample_size)
if ~mod(t,10) if ~mod(t,10)
dyn_waitbar(t/sample_size,hh,'Please wait. Extended Path simulations...'); dyn_waitbar(t/sample_size,hh,'Please wait. Extended Path simulations...');
end end
% Set period index. % Set period index.
t = t+1; t = t+1;
% Put shocks in oo_.exo_simul (second line).
exo_simul_1 = zeros(periods+2,exo_nbr); if replic_nbr > 1 && ep.parallel_1
exo_simul_1(2,positive_var_indx) = oo_.exo_simul(2,positive_var_indx) + oo_.ep.shocks(t,:); parfor k = 1:replic_nbr
if ep.init% Compute first order solution (Perturbation)... exo_simul = repmat(oo_.exo_steady_state',periods+2,1);
initial_path = simult_(initial_conditions,dr,exo_simul_1(2:end,:),1); exo_simul(1:sample_size+3-t,:) = oo_.exo_simul(t:end,:);
endo_simul_1(:,1:end-1) = initial_path(:,1:end-1)*ep.init+endo_simul_1(:,1:end-1)*(1-ep.init); exo_simul(2,positive_var_indx) = exo_simul(2+1,positive_var_indx) + ...
shocks((t-2)*replic_nbr+k,:);
initial_conditions = results{k}(:,t-1);
results{k}(:,t) = extended_path_core(periods,endo_nbr,exo_nbr,positive_var_indx, ...
exo_simul,ep.init,initial_conditions,...
maximum_lag,maximum_lead,steady_state, ...
ep.verbosity,bytecode_flag,ep.stochastic.order,...
M_.params,pfm,ep.stochastic.algo,ep.stock_solve_algo,...
options_.lmmcp);
end
else else
if t==1 for k = 1:replic_nbr
endo_simul_1 = repmat(steady_state,1,periods+2); exo_simul = repmat(oo_.exo_steady_state',periods+maximum_lag+ ...
maximum_lead,1);
exo_simul(1:sample_size+maximum_lag+maximum_lead-t+1,:) = ...
oo_.exo_simul(t:end,:);
exo_simul(maximum_lag+1,positive_var_indx) = ...
exo_simul(maximum_lag+1,positive_var_indx) + shocks((t-2)*replic_nbr+k,:);
initial_conditions = results{k}(:,t-1);
results{k}(:,t) = extended_path_core(periods,endo_nbr,exo_nbr,positive_var_indx, ...
exo_simul,ep.init,initial_conditions,...
maximum_lag,maximum_lead,steady_state, ...
ep.verbosity,bytecode_flag,ep.stochastic.order,...
M_,pfm,ep.stochastic.algo,ep.stack_solve_algo,...
options_.lmmcp);
end end
end end
% Solve a perfect foresight model.
% Keep a copy of endo_simul_1
endo_simul = endo_simul_1;
if verbosity
save ep_test_1 endo_simul_1 exo_simul_1
end
if bytecode_flag && ~options_.ep.stochastic.order
[flag,tmp] = bytecode('dynamic',endo_simul_1,exo_simul_1, M_.params, endo_simul_1, options_.ep.periods);
else
flag = 1;
end
if flag
if options_.ep.stochastic.order == 0
[flag,tmp,err] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm);
else
switch(options_.ep.stochastic.algo)
case 0
[flag,tmp] = ...
solve_stochastic_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm,options_.ep.stochastic.quadrature.nodes,options_.ep.stochastic.order);
case 1
[flag,tmp] = ...
solve_stochastic_perfect_foresight_model_1(endo_simul_1,exo_simul_1,options_,pfm,options_.ep.stochastic.order);
end
end
end
info_convergence = ~flag;
if verbosity
if info_convergence
disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
else
disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
end
end
endo_simul_1 = tmp;
if info_convergence
% Save results of the perfect foresight model solver.
time_series(:,tsimul) = endo_simul_1(:,2);
endo_simul_1(:,1:end-1) = endo_simul_1(:,2:end);
endo_simul_1(:,1) = time_series(:,tsimul);
endo_simul_1(:,end) = oo_.steady_state;
tsimul = tsimul+1;
else
oo_.ep.failures.periods = [oo_.ep.failures.periods t];
oo_.ep.failures.previous_period = [oo_.ep.failures.previous_period endo_simul_1(:,1)];
oo_.ep.failures.shocks = [oo_.ep.failures.shocks shocks];
endo_simul_1 = repmat(steady_state,1,periods+2);
endo_simul_1(:,1) = time_series(:,tsimul-1);
end
end% (while) loop over t end% (while) loop over t
dyn_waitbar_close(hh); dyn_waitbar_close(hh);
@ -242,14 +242,85 @@ dyn_waitbar_close(hh);
if isnan(options_.initial_period) if isnan(options_.initial_period)
initial_period = dates(1,1); initial_period = dates(1,1);
else else
initial_period = optins_.initial_period; initial_period = options_.initial_period;
end end
if nargout if nargout
ts = dseries(transpose([initial_conditions, time_series]),initial_period,cellstr(M_.endo_names)); if ~isnan(results{1})
ts = dseries(transpose([results{1}]), ...
initial_period,cellstr(M_.endo_names));
else
ts = NaN;
end
else else
oo_.endo_simul = [initial_conditions, time_series]; if ~isnan(results{1})
ts = dseries(transpose(oo_.endo_simul),initial_period,cellstr(M_.endo_names)); oo_.endo_simul = results{1};
dyn2vec; ts = dseries(transpose(results{1}),initial_period, ...
cellstr(M_.endo_names));
else
oo_.endo_simul = NaN;
ts = NaN;
end
end end
assignin('base', 'Simulated_time_series', ts); assignin('base', 'Simulated_time_series', ts);
function y = extended_path_core(periods,endo_nbr,exo_nbr,positive_var_indx, ...
exo_simul,init,initial_conditions,...
maximum_lag,maximum_lead,steady_state, ...
verbosity,bytecode_flag,order,M,pfm,algo,stack_solve_algo,...
olmmcp)
if init% Compute first order solution (Perturbation)...
endo_simul = simult_(initial_conditions,dr,exo_simul(2:end,:),1);
else
endo_simul = [initial_conditions repmat(steady_state,1,periods+1)];
end
oo.endo_simul = endo_simul;
oo_.endo_simul = endo_simul;
% Solve a perfect foresight model.
% Keep a copy of endo_simul_1
if verbosity
save ep_test_1 endo_simul exo_simul
end
if bytecode_flag && ~ep.stochastic.order
[flag,tmp] = bytecode('dynamic',endo_simul,exo_simul, M_.params, endo_simul, ep.periods);
else
flag = 1;
end
if flag
if order == 0
options.periods = periods;
options.block = pfm.block;
oo.endo_simul = endo_simul;
oo.exo_simul = exo_simul;
oo.steady_state = steady_state;
options.bytecode = bytecode_flag;
options.lmmcp = olmmcp;
options.stack_solve_algo = stack_solve_algo;
[tmp,flag] = perfect_foresight_solver_core(M,oo,options);
else
switch(algo)
case 0
[flag,tmp] = ...
solve_stochastic_perfect_foresight_model(endo_simul,exo_simul,pfm,ep.stochastic.quadrature.nodes,ep.stochastic.order);
case 1
[flag,tmp] = ...
solve_stochastic_perfect_foresight_model_1(endo_simul,exo_simul,options_,pfm,ep.stochastic.order);
end
end
end
info_convergence = ~flag;
if verbosity
if info_convergence
disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
else
disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
end
end
endo_simul = tmp;
if info_convergence
y = endo_simul(:,2);
else
y = NaN(size(endo_nbr,1));
end

6
matlab/global_initialization.m
View File

@ -187,7 +187,11 @@ ep.innovation_distribution = 'gaussian';
% Set flag for the seed % Set flag for the seed
ep.set_dynare_seed_to_default = 1; ep.set_dynare_seed_to_default = 1;
% Set algorithm for the perfect foresight solver % Set algorithm for the perfect foresight solver
ep.stack_solve_algo = 4; ep.stack_solve_algo = 7;
% Number of replications
ep.replic_nbr = 1;
% Parallel execution of replications
ep.parallel_1 = false;
% Stochastic extended path related options. % Stochastic extended path related options.
ep.stochastic.method = ''; ep.stochastic.method = '';
ep.stochastic.algo = 0; ep.stochastic.algo = 0;