Added new option for perfect foresight simulations (sim1 routine, available only with stack_solve_algo==0).
Try to reduce the size of the nonlinear system of equations by skipping the (last) periods for wich the residuals are already (almost) zero. The number of periods is not constant during the Newton, the effective number of periods for each iteration of the Newton is available in oo_.deterministic_simulation.vperiods.time-shift
Stéphane Adjemian (Charybdis)
parent
49506c6ec4
commit
1ea7631930
|
|
@ -273,6 +273,7 @@ options_.linear = 0;
|
|||
options_.stack_solve_algo = 0;
|
||||
options_.markowitz = 0.5;
|
||||
options_.minimal_solving_periods = 1;
|
||||
options_.endogenous_terminal_period = 0;
|
||||
|
||||
% Solution
|
||||
options_.order = 2;
|
||||
|
|
|
|||
|
|
@ -32,6 +32,9 @@ function sim1()
|
|||
|
||||
global M_ options_ oo_
|
||||
|
||||
endogenous_terminal_period = options_.endogenous_terminal_period;
|
||||
vperiods = options_.periods*ones(1,options_.simul.maxit);
|
||||
|
||||
lead_lag_incidence = M_.lead_lag_incidence;
|
||||
|
||||
ny = M_.endo_nbr;
|
||||
|
|
@ -79,7 +82,6 @@ z = Y(find(lead_lag_incidence'));
|
|||
|
||||
A = sparse([],[],[],periods*ny,periods*ny,periods*nnz(jacobian));
|
||||
res = zeros(periods*ny,1);
|
||||
|
||||
|
||||
h1 = clock ;
|
||||
for iter = 1:options_.simul.maxit
|
||||
|
|
@ -88,11 +90,10 @@ for iter = 1:options_.simul.maxit
|
|||
i_rows = 1:ny;
|
||||
i_cols = find(lead_lag_incidence');
|
||||
i_cols_A = i_cols;
|
||||
|
||||
|
||||
for it = (M_.maximum_lag+1):(M_.maximum_lag+periods)
|
||||
|
||||
[d1,jacobian] = model_dynamic(Y(i_cols),exo_simul, params, ...
|
||||
steady_state,it);
|
||||
[d1,jacobian] = model_dynamic(Y(i_cols), exo_simul, params, steady_state,it);
|
||||
if it == M_.maximum_lag+periods && it == M_.maximum_lag+1
|
||||
A(i_rows,i_cols_A0) = jacobian(:,i_cols_0);
|
||||
elseif it == M_.maximum_lag+periods
|
||||
|
|
@ -105,8 +106,17 @@ for iter = 1:options_.simul.maxit
|
|||
|
||||
res(i_rows) = d1;
|
||||
|
||||
if endogenous_terminal_period && iter>1
|
||||
dr = max(abs(d1));
|
||||
if 10000*dr<options_.dynatol.f && 100*max(abs(res(i_rows(end)+1:end)))<options_.dynatol.f
|
||||
vperiods(iter) = it;
|
||||
break
|
||||
end
|
||||
end
|
||||
|
||||
i_rows = i_rows + ny;
|
||||
i_cols = i_cols + ny;
|
||||
|
||||
if it > M_.maximum_lag+1
|
||||
i_cols_A = i_cols_A + ny;
|
||||
end
|
||||
|
|
@ -125,7 +135,12 @@ for iter = 1:options_.simul.maxit
|
|||
break
|
||||
end
|
||||
|
||||
dy = -A\res;
|
||||
if endogenous_terminal_period && iter>1
|
||||
dy = zeros(length(i_upd),1);
|
||||
dy(1:i_rows(end)) = -A(1:i_rows(end),1:i_rows(end))\res(1:i_rows(end));
|
||||
else
|
||||
dy = -A\res;
|
||||
end
|
||||
|
||||
Y(i_upd) = Y(i_upd) + dy;
|
||||
|
||||
|
|
@ -137,6 +152,7 @@ if stop
|
|||
oo_.deterministic_simulation.status = 0;% NaN or Inf occurred
|
||||
oo_.deterministic_simulation.error = err;
|
||||
oo_.deterministic_simulation.iterations = iter;
|
||||
oo_.deterministic_simulation.periods = vperiods(1:iter);
|
||||
oo_.endo_simul = reshape(Y,ny,periods+2);
|
||||
skipline();
|
||||
fprintf('\nSimulation terminated after %d iterations.\n',iter);
|
||||
|
|
@ -150,6 +166,7 @@ if stop
|
|||
oo_.deterministic_simulation.status = 1;% Convergency obtained.
|
||||
oo_.deterministic_simulation.error = err;
|
||||
oo_.deterministic_simulation.iterations = iter;
|
||||
oo_.deterministic_simulation.periods = vperiods(1:iter);
|
||||
oo_.endo_simul = reshape(Y,ny,periods+2);
|
||||
end
|
||||
elseif ~stop
|
||||
|
|
@ -159,9 +176,9 @@ elseif ~stop
|
|||
fprintf('WARNING : maximum number of iterations is reached (modify options_.simul.maxit).\n') ;
|
||||
oo_.deterministic_simulation.status = 0;% more iterations are needed.
|
||||
oo_.deterministic_simulation.error = err;
|
||||
oo_.deterministic_simulation.periods = vperiods(1:iter);
|
||||
%oo_.deterministic_simulation.errors = c/abs(err)
|
||||
oo_.deterministic_simulation.iterations = options_.simul.maxit;
|
||||
end
|
||||
disp (['-----------------------------------------------------']) ;
|
||||
skipline();
|
||||
|
||||
skipline();
|
||||
|
|
@ -122,7 +122,7 @@ class ParsingDriver;
|
|||
%token <string_val> QUOTED_STRING
|
||||
%token QZ_CRITERIUM QZ_ZERO_THRESHOLD FULL DSGE_VAR DSGE_VARLAG DSGE_PRIOR_WEIGHT TRUNCATE
|
||||
%token RELATIVE_IRF REPLIC SIMUL_REPLIC RPLOT SAVE_PARAMS_AND_STEADY_STATE PARAMETER_UNCERTAINTY
|
||||
%token SHOCKS SHOCK_DECOMPOSITION SIGMA_E SIMUL SIMUL_ALGO SIMUL_SEED SIMULATION_TYPE
|
||||
%token SHOCKS SHOCK_DECOMPOSITION SIGMA_E SIMUL SIMUL_ALGO SIMUL_SEED SIMULATION_TYPE ENDOGENOUS_TERMINAL_PERIOD
|
||||
%token SMOOTHER SQUARE_ROOT_SOLVER STACK_SOLVE_ALGO STEADY_STATE_MODEL STOCHASTIC SOLVE_ALGO SOLVER_PERIODS
|
||||
%token STDERR STEADY STOCH_SIMUL SURPRISE SYLVESTER SYLVESTER_FIXED_POINT_TOL REGIMES REGIME
|
||||
%token TEX RAMSEY_POLICY PLANNER_DISCOUNT DISCRETIONARY_POLICY DISCRETIONARY_TOL
|
||||
|
|
@ -986,6 +986,7 @@ simul_options : o_periods
|
|||
| o_markowitz
|
||||
| o_minimal_solving_periods
|
||||
| o_simul_maxit
|
||||
| o_endogenous_terminal_period
|
||||
;
|
||||
|
||||
external_function : EXTERNAL_FUNCTION '(' external_function_options_list ')' ';'
|
||||
|
|
@ -2329,6 +2330,7 @@ o_simul_algo : SIMUL_ALGO EQUAL INT_NUMBER {
|
|||
driver.error("simul_algo=1 option is no longer supported");
|
||||
};
|
||||
o_stack_solve_algo : STACK_SOLVE_ALGO EQUAL INT_NUMBER { driver.option_num("stack_solve_algo", $3); };
|
||||
o_endogenous_terminal_period : ENDOGENOUS_TERMINAL_PERIOD { driver.option_num("endogenous_terminal_period", "1"); };
|
||||
o_linear : LINEAR { driver.linear(); };
|
||||
o_order : ORDER EQUAL INT_NUMBER { driver.option_num("order", $3); };
|
||||
o_replic : REPLIC EQUAL INT_NUMBER { driver.option_num("replic", $3); };
|
||||
|
|
|
|||
|
|
@ -295,6 +295,7 @@ DATE -?[0-9]+([YyAa]|[Mm]([1-9]|1[0-2])|[Qq][1-4]|[Ww]([1-9]{1}|[1-4][0-9]|5[0-2
|
|||
<DYNARE_STATEMENT>nocorr {return token::NOCORR;}
|
||||
<DYNARE_STATEMENT>optim {return token::OPTIM;}
|
||||
<DYNARE_STATEMENT>periods {return token::PERIODS;}
|
||||
<DYNARE_STATEMENT>endogenous_terminal_period {return token::ENDOGENOUS_TERMINAL_PERIOD;}
|
||||
<DYNARE_STATEMENT>sub_draws {return token::SUB_DRAWS;}
|
||||
<DYNARE_STATEMENT>minimal_solving_periods {return token::MINIMAL_SOLVING_PERIODS;}
|
||||
<DYNARE_STATEMENT>markowitz {return token::MARKOWITZ;}
|
||||
|
|
|
|||
|
|
@ -150,6 +150,7 @@ MODFILES = \
|
|||
deterministic_simulations/rbc_det3.mod \
|
||||
deterministic_simulations/rbc_det4.mod \
|
||||
deterministic_simulations/rbc_det5.mod \
|
||||
deterministic_simulations/rbc_det6.mod \
|
||||
walsh.mod \
|
||||
measurement_errors/fs2000_corr_me_ml_mcmc/fs2000_corr_ME.mod \
|
||||
trend_var/fs2000_nonstationary.mod \
|
||||
|
|
|
|||
|
|
@ -0,0 +1,85 @@
|
|||
var Capital, Output, Labour, Consumption, Efficiency, efficiency, ExpectedTerm;
|
||||
|
||||
varexo EfficiencyInnovation;
|
||||
|
||||
parameters beta, theta, tau, alpha, psi, delta, rho, effstar, sigma2;
|
||||
|
||||
beta = 0.9900;
|
||||
theta = 0.3570;
|
||||
tau = 2.0000;
|
||||
alpha = 0.4500;
|
||||
psi = -0.1000;
|
||||
delta = 0.0200;
|
||||
rho = 0.8000;
|
||||
effstar = 1.0000;
|
||||
sigma2 = 0;
|
||||
|
||||
model(use_dll);
|
||||
|
||||
// Eq. n°1:
|
||||
efficiency = rho*efficiency(-1) + EfficiencyInnovation;
|
||||
|
||||
// Eq. n°2:
|
||||
Efficiency = effstar*exp(efficiency);
|
||||
|
||||
// Eq. n°3:
|
||||
Output = Efficiency*(alpha*(Capital(-1)^psi)+(1-alpha)*(Labour^psi))^(1/psi);
|
||||
|
||||
// Eq. n°4:
|
||||
Capital = Output-Consumption + (1-delta)*Capital(-1);
|
||||
|
||||
// Eq. n°5:
|
||||
((1-theta)/theta)*(Consumption/(1-Labour)) - (1-alpha)*(Output/Labour)^(1-psi);
|
||||
|
||||
// Eq. n°6:
|
||||
(((Consumption^theta)*((1-Labour)^(1-theta)))^(1-tau))/Consumption = ExpectedTerm(1);
|
||||
|
||||
// Eq. n°7:
|
||||
ExpectedTerm = beta*((((Consumption^theta)*((1-Labour)^(1-theta)))^(1-tau))/Consumption)*(alpha*((Output/Capital(-1))^(1-psi))+(1-delta));
|
||||
|
||||
end;
|
||||
|
||||
steady_state_model;
|
||||
efficiency = EfficiencyInnovation/(1-rho);
|
||||
Efficiency = effstar*exp(efficiency);
|
||||
Output_per_unit_of_Capital=((1/beta-1+delta)/alpha)^(1/(1-psi));
|
||||
Consumption_per_unit_of_Capital=Output_per_unit_of_Capital-delta;
|
||||
Labour_per_unit_of_Capital=(((Output_per_unit_of_Capital/Efficiency)^psi-alpha)/(1-alpha))^(1/psi);
|
||||
Output_per_unit_of_Labour=Output_per_unit_of_Capital/Labour_per_unit_of_Capital;
|
||||
Consumption_per_unit_of_Labour=Consumption_per_unit_of_Capital/Labour_per_unit_of_Capital;
|
||||
|
||||
% Compute steady state share of capital.
|
||||
ShareOfCapital=alpha/(alpha+(1-alpha)*Labour_per_unit_of_Capital^psi);
|
||||
|
||||
% Compute steady state of the endogenous variables.
|
||||
Labour=1/(1+Consumption_per_unit_of_Labour/((1-alpha)*theta/(1-theta)*Output_per_unit_of_Labour^(1-psi)));
|
||||
Consumption=Consumption_per_unit_of_Labour*Labour;
|
||||
Capital=Labour/Labour_per_unit_of_Capital;
|
||||
Output=Output_per_unit_of_Capital*Capital;
|
||||
ExpectedTerm=beta*((((Consumption^theta)*((1-Labour)^(1-theta)))^(1-tau))/Consumption)
|
||||
*(alpha*((Output/Capital)^(1-psi))+1-delta);
|
||||
end;
|
||||
|
||||
steady;
|
||||
|
||||
ik = varlist_indices('Capital',M_.endo_names);
|
||||
CapitalSS = oo_.steady_state(ik);
|
||||
|
||||
histval;
|
||||
Capital(0) = CapitalSS/2;
|
||||
end;
|
||||
|
||||
//options_.endogenous_terminal_period = 0;
|
||||
simul(periods=500);
|
||||
fff = oo_.endo_simul;
|
||||
|
||||
oo_.deterministic_simulation
|
||||
|
||||
//options_.endogenous_terminal_period = 1;
|
||||
simul(periods=500, endogenous_terminal_period);
|
||||
ggg = oo_.endo_simul;
|
||||
|
||||
oo_.deterministic_simulation
|
||||
|
||||
t1 = fff-ggg;
|
||||
t2 = max(max(t1));
|
||||
Loading…
Reference in New Issue