Streamlined extended path examples.

tests/ep/ar.mod

@@ -15,7 +15,7 @@ sigma   =  0.0001;
 
 external_function(name=mean_preserving_spread,nargs=2);
 
-model;
+model(use_dll);
 
   // Eq. n°1:
   efficiency = rho*efficiency(-1) + sigma*EfficiencyInnovation;
@@ -31,21 +31,14 @@ end;
 
 steady;
 
-options_.ep.verbosity = 0;
 options_.ep.stochastic.order = 0;
-options_.ep.stochastic.nodes = 0;
-options_.console_mode = 0;
+ts = extended_path([], 100, [], options_, M_, oo_);
 
-ts = extended_path([], 10, options_, M_, oo_);
-
-options_.ep.verbosity = 0;
 options_.ep.stochastic.order = 1;
-options_.ep.IntegrationAlgorithm='Tensor-Gaussian-Quadrature';
-options_.ep.stochastic.nodes = 3;
-options_.console_mode = 0;
+sts = extended_path([], 100, [], options_, M_, oo_);
 
-sts = extended_path([], 10, [], options_, M_, oo_);
-
-if max(max(abs(ts-sts)))>pi*options_.dynatol.x
+// The model is backward, we do not care about future uncertainty, extended path and stochastic extended path
+// should return the same results.
+if max(max(abs(ts.data-sts.data)))>pi*options_.dynatol.x
    disp('Stochastic Extended Path:: Something is wrong here (potential bug in extended_path.m)!!!')
 end

tests/ep/burnside.mod

@@ -32,34 +32,51 @@ if beta*exp(theta*xbar+.5*theta^2*M_.Sigma_e/(1-rho)^2)>1-eps
 end
 
 set_dynare_seed('default');
-stoch_simul(order=1,irf=0,periods=5000);
+stoch_simul(order=1,irf=0,periods=201);
 y_perturbation_1 = oo_.endo_simul(1,:)';
 
 set_dynare_seed('default');
-stoch_simul(order=2,irf=0,periods=5000);
+stoch_simul(order=2,irf=0,periods=201);
 y_perturbation_2 = oo_.endo_simul(1,:)';
 
 set_dynare_seed('default');
-stoch_simul(order=2,pruning,irf=0,periods=5000);
+stoch_simul(order=2,pruning,irf=0,periods=201);
 y_perturbation_2_pruning = oo_.endo_simul(1,:)';
 
 options_.simul.maxit = 100;
-options_.ep.verbosity = 0;
-options_.ep.stochastic.order = 0;
-options_.ep.stochastic.nodes = 2;
-options_.console_mode = 0;
-
-set_dynare_seed('default');
-ts = extended_path([], 5000, [], options_, M_, oo_);
-
-options_.ep.stochastic.order = 2;
-options_.ep.IntegrationAlgorithm='Tensor-Gaussian-Quadrature';
-set_dynare_seed('default');
-ts1_4 = extended_path([], 5000, [], options_, M_, oo_);
 
 set_dynare_seed('default');
 ytrue=exact_solution(M_,oo_, 800);
 
+
+set_dynare_seed('default');
+options_.ep.stochastic.order = 0;
+ts = extended_path([], 200, [], options_, M_, oo_);
+
+set_dynare_seed('default');
+options_.ep.stochastic.order = 2;
+options_.ep.IntegrationAlgorithm='Stroud-Cubature-3';//'Unscented'; //'Tensor-Gaussian-Quadrature';
+options_.ep.stochastic.quadrature.nodes = 3;
+ts1_4 = extended_path([], 200, [], options_, M_, oo_);
+
+set_dynare_seed('default');
+options_.ep.stochastic.order = 4;
+options_.ep.IntegrationAlgorithm='Stroud-Cubature-3';//'Unscented'; //'Tensor-Gaussian-Quadrature';
+options_.ep.stochastic.quadrature.nodes = 3;
+ts1_8 = extended_path([], 200, [], options_, M_, oo_);
+
+set_dynare_seed('default');
+options_.ep.stochastic.order = 6;
+options_.ep.IntegrationAlgorithm='Stroud-Cubature-3';//'Unscented'; //'Tensor-Gaussian-Quadrature';
+options_.ep.stochastic.quadrature.nodes = 3;
+ts1_12 = extended_path([], 200, [], options_, M_, oo_);
+
+set_dynare_seed('default');
+options_.ep.stochastic.order = 8;
+options_.ep.IntegrationAlgorithm='Stroud-Cubature-3';//'Unscented'; //'Tensor-Gaussian-Quadrature';
+options_.ep.stochastic.quadrature.nodes = 3;
+ts1_16 = extended_path([], 200, [], options_, M_, oo_);
+
 disp('True mean and standard deviation')
 disp(mean(ytrue(101:end)))
 disp(sqrt(var(ytrue(101:end))))
@@ -77,12 +94,12 @@ disp(mean(y_perturbation_2_pruning(101:end)))
 disp(sqrt(var(y_perturbation_2_pruning(101:end))))
 
 disp('Extended path mean and standard deviation')
-disp(mean(ts(1,101:end)))
-disp(sqrt(var(ts(1,101:end))))
+disp(mean(ts.data(101:end,1)))
+disp(sqrt(var(ts.data(101:end,1))))
 
 disp('Stochastic extended path mean and standard deviation')
-disp(mean(ts1_4(1,101:end)))
-disp(sqrt(var(ts1_4(1,101:end))))
+disp(mean(ts1_4.data(101:end,1)))
+disp(sqrt(var(ts1_4.data(101:end,1))))
 
 disp('Accuracy error (order 1 perturbation)')
 disp(mean(100*abs(y_perturbation_1-ytrue')./ytrue'));
@@ -97,9 +114,21 @@ disp(100*mean(abs(y_perturbation_2_pruning-ytrue')./ytrue'));
 disp(100*max(abs(y_perturbation_2_pruning-ytrue')./ytrue'));
 
 disp('Accuracy error (extended path)')
-disp(mean(100*abs(ts(1,:)'-ytrue')./ytrue'));
-disp(max(100*abs(ts(1,:)'-ytrue')./ytrue'));
+disp(mean(100*abs(ts.data(:,1)-ytrue')./ytrue'));
+disp(max(100*abs(ts.data(:,1)-ytrue')./ytrue'));
 
-disp('Accuracy error (stochastic extended path)')
-disp(mean(100*abs(ts1_4(1,:)'-ytrue')./ytrue'));
-disp(max(100*abs(ts1_4(1,:)'-ytrue')./ytrue'));
+disp('Accuracy error (stochastic extended path (order=2))')
+disp(mean(100*abs(ts1_4.data(:,1)-ytrue')./ytrue'));
+disp(max(100*abs(ts1_4.data(:,1)-ytrue')./ytrue'));
+
+disp('Accuracy error (stochastic extended path (order=4))')
+disp(mean(100*abs(ts1_8.data(:,1)-ytrue')./ytrue'));
+disp(max(100*abs(ts1_8.data(:,1)-ytrue')./ytrue'));
+
+disp('Accuracy error (stochastic extended path (order=6))')
+disp(mean(100*abs(ts1_12.data(:,1)-ytrue')./ytrue'));
+disp(max(100*abs(ts1_12.data(:,1)-ytrue')./ytrue'));
+
+disp('Accuracy error (stochastic extended path (order=8))')
+disp(mean(100*abs(ts1_16.data(:,1)-ytrue')./ytrue'));
+disp(max(100*abs(ts1_16.data(:,1)-ytrue')./ytrue'));

tests/ep/linearmodel0.mod

@@ -31,11 +31,7 @@ stoch_simul(order=1,periods=200, irf=0, nomoments);
 oo = oo_;
 
 options_.simul.maxit = 100;
-options_.ep.verbosity = 0;
-options_.ep.stochastic.status = 0;
 options_.ep.order = 0;
-options_.ep.nnodes = 0;
-options_.console_mode = 0;
 
 ts = extended_path([], 200, oo_.exo_simul, options_, M_, oo_);
 

tests/ep/linearmodel1.mod

@@ -26,22 +26,14 @@ end;
 
 steady;
 
-options_.simul.maxit = 100;
-options_.ep.verbosity = 0;
-options_.ep.stochastic.status = 0;
-options_.ep.order = 0;
-options_.ep.nnodes = 0;
-options_.console_mode = 0;
 
 // Extended path simulation
-ts = extended_path([], 10, [], options_, M_, oo_);
+options_.ep.order = 0;
+ts = extended_path([], 100, [], options_, M_, oo_);
 
 // Stochastic extended path simulation
-options_.ep.stochastic.status = 1;
-options_.ep.IntegrationAlgorithm='Tensor-Gaussian-Quadrature';
 options_.ep.order = 1;
-options_.ep.nnodes = 3;
-sts = extended_path([], 10, [], options_, M_, oo_);
+sts = extended_path([], 100, [], options_, M_, oo_);
 
 // The generated paths should be identical (because the model is linear)
 if max(max(abs(ts.data-sts.data))) > 1e-12

tests/ep/rbc2.mod

@@ -76,6 +76,5 @@ end;
 
 steady;
 
-options_.ep.IntegrationAlgorithm='Tensor-Gaussian-Quadrature';
 extended_path(periods=10);
 

tests/ep/rbcii.mod

@@ -66,20 +66,13 @@ copyfile('rbcii_steady_state.m','+rbcii/steadystate.m');
 
     steady(nocheck);
 
-    options_.simul.maxit = 100;
-    options_.ep.verbosity = 0;
     options_.ep.stochastic.order = 0;
-    options_.ep.stochastic.nodes = 2;
-    options_.console_mode = 0;
 
     ts = extended_path([], 20, [], options_, M_, oo_);
 
     options_.ep.stochastic.order = 1;
-    options_.ep.IntegrationAlgorithm='Tensor-Gaussian-Quadrature';
-//    profile on
     ts1_4 = extended_path([], 20, [], options_, M_, oo_);
-//    profile off
-//    profile viewer
+
 @#else
 
     shocks;

tests/ep/rs2.mod

@@ -50,7 +50,7 @@ parameters theta xi beta phi alpha eta KBar chi0 LMax chi IBar rhoinflavg taylrh
   VAIMSS = 1;
   consoldelta = 1;
 
-model;//(use_dll);
+model(use_dll);
 // Value function and Euler equation
 V = exp(lC)^(1-phi) /(1-phi) + chi0 *(LMax-exp(lL))^(1-chi) /(1-chi) + beta *Vkp;
 Int1 = Int/10;
@@ -152,12 +152,5 @@ end;
 
 //stoch_simul(order=3,periods=50000,pruning);
 
-options_.ep.verbosity=0;
-options_.ep.stochastic.algo=1;
-options_.ep.solve_algo = 10;
-options_.ep.maxit = 100;
-options_.ep.IntegrationAlgorithm='UT_2p+1';
-options_.ep.ut.k = 1;
-options_.solve_tolf = 1e-12;
 
-extended_path(order=1,periods=3);
+extended_path(order=0,periods=3);