tests/conditional_forecasts/1/fs2000_cal.mod

@@ -78,5 +78,22 @@ end;
 
 conditional_forecast(parameter_set=calibration, controlled_varexo=(e_a,e_m));
 
+results_1=struct2array(oo_.conditional_forecast.cond.Mean);
+
 plot_conditional_forecast(periods=10) gy_obs gp_obs;
+xx=[0.01 -0.02 0 0 0];
+conditional_forecast_paths;
+var gy_obs;
+periods  1:5;
+values   (xx);
+var gp_obs;
+periods 1:5;
+values  0.05;
+end;
+
+conditional_forecast(parameter_set=calibration, controlled_varexo=(e_a,e_m));
+results_2=struct2array(oo_.conditional_forecast.cond.Mean);
 
+if max(max(abs(results_1-results_2)))>1e-10
+    error('Interface wrong')
+end

tests/deterministic_simulations/homotopy.mod

@@ -31,8 +31,6 @@ steady_state_model;
   Consumption = exp(LoggedProductivity)*Capital^alpha-delta*Capital;
 end;
 
-set_time(1Q1);
-
 initval;
   LoggedProductivityInnovation = 0;
 end;
@@ -45,7 +43,7 @@ end;
 
 steady;
 
-perfect_foresight_setup(periods=200);
+perfect_foresight_setup(periods=200, first_simulation_period = 1Q2);
 perfect_foresight_solver;
 
 if ~oo_.deterministic_simulation.status

tests/deterministic_simulations/homotopy_endval_steady_linearization.mod

 // Example that triggers homotopy in perfect foresight simulation.
 // Tests the endval_steady, homotopy_linearization_fallback options, and a few more.
 
-var Consumption, Capital, LoggedProductivity;
-
-varexo LoggedProductivityInnovation;
-
-parameters beta, alpha, delta, rho;
-
-beta = .985;
-alpha = 1/3;
-delta = alpha/10;
-rho = .9;
-
-model;
-  1/Consumption = beta/Consumption(1)*(alpha*exp(LoggedProductivity(1))*Capital^(alpha-1)+1-delta);
-  Capital = exp(LoggedProductivity)*Capital(-1)^alpha+(1-delta)*Capital(-1)-Consumption;
-  LoggedProductivity = rho*LoggedProductivity(-1)+LoggedProductivityInnovation;
-end;
-
-initval;
-  LoggedProductivityInnovation = 0;
-end;
-
-steady;
-
-endval;
-  LoggedProductivityInnovation = 1;
-  Consumption = 0.1;
-  Capital = 1;
-end;
+@#include "homotopy_linearization.inc"
 
 perfect_foresight_setup(periods=200, endval_steady);
 

tests/deterministic_simulations/homotopy_histval.mod

@@ -31,8 +31,6 @@ steady_state_model;
   Consumption = exp(LoggedProductivity)*Capital^alpha-delta*Capital;
 end;
 
-set_time(1Q1);
-
 initval;
   LoggedProductivityInnovation = 0;
   LoggedProductivity = 10;
@@ -46,7 +44,7 @@ histval;
  LoggedProductivity(0)=10;
 end;
 
-perfect_foresight_setup(periods=200);
+perfect_foresight_setup(periods=200, first_simulation_period = 1Q2);
 perfect_foresight_solver;
 
 if ~oo_.deterministic_simulation.status

tests/deterministic_simulations/homotopy_linearization.inc

+var Consumption, Capital, LoggedProductivity;
+
+varexo LoggedProductivityInnovation;
+
+parameters beta, alpha, delta, rho;
+
+beta = .985;
+alpha = 1/3;
+delta = alpha/10;
+rho = .9;
+
+model;
+  1/Consumption = beta/Consumption(1)*(alpha*exp(LoggedProductivity(1))*Capital^(alpha-1)+1-delta);
+  Capital = exp(LoggedProductivity)*Capital(-1)^alpha+(1-delta)*Capital(-1)-Consumption;
+  LoggedProductivity = rho*LoggedProductivity(-1)+LoggedProductivityInnovation;
+end;
+
+initval;
+  LoggedProductivityInnovation = 0;
+end;
+
+steady;
+
+endval;
+  LoggedProductivityInnovation = 1;
+  Consumption = 0.1;
+  Capital = 1;
+end;

tests/deterministic_simulations/homotopy_linearization_condpaths.mod

+// Example that triggers homotopy in perfect foresight simulation.
+// Tests homotopy_linearization_fallback with perfect_foresight_controlled_paths
+
+@#include "homotopy_linearization.inc"
+
+perfect_foresight_controlled_paths;
+  exogenize LoggedProductivity;
+  periods 8:9;
+  values 5;
+  endogenize LoggedProductivityInnovation;
+end;
+
+perfect_foresight_setup(periods=200, endval_steady);
+
+perfect_foresight_solver(homotopy_max_completion_share = 0.7,
+                         homotopy_linearization_fallback,
+                         steady_solve_algo = 13);
+
+if ~oo_.deterministic_simulation.status
+   error('Perfect foresight simulation failed')
+end
+
+if ~(all(abs(oo_.endo_simul(3, 9:10) - 5) < 1e-15) && abs(oo_.exo_simul(10) - 0.5) < 1e-15)
+   error('Homotopy with linearization and controlled paths failed')
+end

tests/deterministic_simulations/homotopy_marginal_linearization.mod

 // Example that triggers homotopy in perfect foresight simulation.
 // Tests the homotopy_marginal_linearization_fallback and homotopy_step_size_increase_success_count options
 
-var Consumption, Capital, LoggedProductivity;
-
-varexo LoggedProductivityInnovation;
-
-parameters beta, alpha, delta, rho;
-
-beta = .985;
-alpha = 1/3;
-delta = alpha/10;
-rho = .9;
-
-model;
-  1/Consumption = beta/Consumption(1)*(alpha*exp(LoggedProductivity(1))*Capital^(alpha-1)+1-delta);
-  Capital = exp(LoggedProductivity)*Capital(-1)^alpha+(1-delta)*Capital(-1)-Consumption;
-  LoggedProductivity = rho*LoggedProductivity(-1)+LoggedProductivityInnovation;
-end;
-
-initval;
-  LoggedProductivityInnovation = 0;
-end;
-
-steady;
-
-endval;
-  LoggedProductivityInnovation = 1;
-  Consumption = 0.1;
-  Capital = 1;
-end;
+@#include "homotopy_linearization.inc"
 
 perfect_foresight_setup(periods=200, endval_steady);
 

tests/deterministic_simulations/homotopy_marglinearization_condpaths.mod

+// Example that triggers homotopy in perfect foresight simulation.
+// Tests homotopy_marginal_linearization_fallback with perfect_foresight_controlled_paths
+
+@#include "homotopy_linearization.inc"
+
+perfect_foresight_controlled_paths;
+  exogenize LoggedProductivity;
+  periods 8:9;
+  values 5;
+  endogenize LoggedProductivityInnovation;
+end;
+
+perfect_foresight_setup(periods=200, endval_steady);
+
+perfect_foresight_solver(homotopy_max_completion_share = 0.7,
+                         homotopy_marginal_linearization_fallback,
+                         steady_solve_algo = 13);
+
+if ~oo_.deterministic_simulation.status
+   error('Perfect foresight simulation failed')
+end
+
+if ~(all(abs(oo_.endo_simul(3, 9:10) - 5) < 1e-14) && abs(oo_.exo_simul(10) - 0.5) < 1e-13)
+   error('Homotopy with marginal linearization and controlled paths failed')
+end

tests/deterministic_simulations/linear_state_space_arma_condpaths.mod

+% Tests perfect simulation of linear model (sim1_linear.m) with controlled paths
+
+var x
+    y
+    z;
+
+varexo u
+       v;
+
+parameters a1 a2 a3 a4
+	   b1 b2 b3
+	   c1;
+
+a1 =  .50;
+a2 =  .00;
+a3 =  .70;
+a4 =  .40;
+b1 =  .90;
+b2 =  .00;
+b3 =  .80;
+c1 =  .95;
+
+
+model(linear);
+  y = a1*x(-1) + a2*x(+1) + a3*z + a4*y(-1);
+  z = b1*z(-1) + b2*z(+1) + b3*x + u;
+  x = c1*x(-1) + v +v(-1)+v(+1);
+end;
+
+initval;
+  y=-1;
+  x=-1;
+  z=-1;
+end;
+
+endval;
+  y=0;
+  x=0;
+  z=0;
+end;
+
+perfect_foresight_controlled_paths;
+  exogenize z;
+  periods 50;
+  values 0.5;
+  endogenize u;
+end;
+
+steady;
+
+perfect_foresight_setup(periods=100);
+perfect_foresight_solver;
+
+if ~oo_.deterministic_simulation.status
+   error('Perfect foresight simulation failed')
+end
+
+if ~(oo_.endo_simul(3, 51) == 0.5 && isequal(find(oo_.exo_simul ~= 0), 51))
+   error('Linear perfect foresight simulation with controlled paths failed')
+end

tests/deterministic_simulations/pfwee.mod

@@ -37,29 +37,30 @@ perfect_foresight_with_expectation_errors_solver;
 pfwee_simul = oo_.endo_simul;
 
 // Now compute the solution by hand to verify the results
+verbatim;
+
 oo_.steady_state = orig_steady_state;
 oo_.exo_steady_state = orig_exo_steady_state;
 
-perfect_foresight_setup;
-
-verbatim;
+oo_ = make_ex_(M_,options_,oo_);
+oo_ = make_y_(M_,options_,oo_);
 
 % Information arriving in period 1 (temp shock now)
 oo_.exo_simul(2,1) = 1.2;
-oo_=perfect_foresight_solver(M_, options_, oo_, true);
+oo_=perfect_foresight_solver(M_, options_, oo_);
 
 % Information arriving in period 2 (temp shock now + permanent shock in future)
 oo_.exo_simul(3,1) = 1.3;
 oo_.exo_steady_state = 1.1;
 oo_.exo_simul(end, 1) = oo_.exo_steady_state;
-oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_, true);
+oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_);
 oo_.endo_simul(:, end) = oo_.steady_state;
 options_.periods = 6;
 saved_endo = oo_.endo_simul(:, 1);
 saved_exo = oo_.exo_simul(1, :);
 oo_.endo_simul = oo_.endo_simul(:, 2:end);
 oo_.exo_simul = oo_.exo_simul(2:end, :);
-oo_=perfect_foresight_solver(M_, options_, oo_, true);
+oo_=perfect_foresight_solver(M_, options_, oo_);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 
@@ -67,14 +68,14 @@ oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 oo_.exo_simul(4,1) = 1.4;
 oo_.exo_steady_state = 1.2;
 oo_.exo_simul(end, 1) = oo_.exo_steady_state;
-oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_, true);
+oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_);
 oo_.endo_simul(:, end) = oo_.steady_state;
 options_.periods = 5;
 saved_endo = oo_.endo_simul(:, 1:2);
 saved_exo = oo_.exo_simul(1:2, :);
 oo_.endo_simul = oo_.endo_simul(:, 3:end);
 oo_.exo_simul = oo_.exo_simul(3:end, :);
-oo_=perfect_foresight_solver(M_, options_, oo_, true);
+oo_=perfect_foresight_solver(M_, options_, oo_);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 
@@ -83,14 +84,14 @@ oo_.exo_simul(7,1) = 1.1;
 oo_.exo_simul(8,1) = 1.1;
 oo_.exo_steady_state = 1.1;
 oo_.exo_simul(end, 1) = oo_.exo_steady_state;
-oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_, true);
+oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_);
 oo_.endo_simul(:, end) = oo_.steady_state;
 options_.periods = 2;
 saved_endo = oo_.endo_simul(:, 1:5);
 saved_exo = oo_.exo_simul(1:5, :);
 oo_.endo_simul = oo_.endo_simul(:, 6:end);
 oo_.exo_simul = oo_.exo_simul(6:end, :);
-oo_=perfect_foresight_solver(M_, options_, oo_,true);
+oo_=perfect_foresight_solver(M_, options_, oo_);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 

tests/deterministic_simulations/pfwee_constant_sim_length.mod

@@ -36,16 +36,17 @@ perfect_foresight_with_expectation_errors_solver(constant_simulation_length);
 pfwee_simul = oo_.endo_simul;
 
 // Now compute the solution by hand to verify the results
+verbatim;
+
 oo_.steady_state = orig_steady_state;
 oo_.exo_steady_state = orig_exo_steady_state;
 
-perfect_foresight_setup;
-
-verbatim;
+oo_ = make_ex_(M_,options_,oo_);
+oo_ = make_y_(M_,options_,oo_);
 
 % Information arriving in period 1 (temp shock now)
 oo_.exo_simul(2,1) = 1.2;
-oo_=perfect_foresight_solver(M_, options_, oo_, true);
+oo_=perfect_foresight_solver(M_, options_, oo_);
 
 % Information arriving in period 2 (temp shock now + permanent shock in future)
 oo_.exo_simul(3,1) = 1.3;
@@ -57,7 +58,7 @@ saved_endo = oo_.endo_simul(:, 1);
 saved_exo = oo_.exo_simul(1, :);
 oo_.endo_simul = oo_.endo_simul(:, 2:end);
 oo_.exo_simul = oo_.exo_simul(2:end, :);
-oo_=perfect_foresight_solver(M_, options_, oo_, true);
+oo_=perfect_foresight_solver(M_, options_, oo_);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 
@@ -71,7 +72,7 @@ saved_endo = oo_.endo_simul(:, 1:2);
 saved_exo = oo_.exo_simul(1:2, :);
 oo_.endo_simul = oo_.endo_simul(:, 3:end);
 oo_.exo_simul = oo_.exo_simul(3:end, :);
-oo_=perfect_foresight_solver(M_, options_, oo_, true);
+oo_=perfect_foresight_solver(M_, options_, oo_);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 
@@ -87,7 +88,7 @@ saved_endo = oo_.endo_simul(:, 1:5);
 saved_exo = oo_.exo_simul(1:5, :);
 oo_.endo_simul = oo_.endo_simul(:, 6:end);
 oo_.exo_simul = oo_.exo_simul(6:end, :);
-oo_=perfect_foresight_solver(M_, options_, oo_, true);
+oo_=perfect_foresight_solver(M_, options_, oo_);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 

tests/deterministic_simulations/pfwee_homotopy.inc

+var Consumption, Capital, LoggedProductivity;
+
+varexo LoggedProductivityInnovation;
+
+parameters beta, alpha, delta, rho;
+
+beta = .985;
+alpha = 1/3;
+delta = alpha/10;
+rho = .9;
+
+model;
+  1/Consumption = beta/Consumption(1)*(alpha*exp(LoggedProductivity(1))*Capital^(alpha-1)+1-delta);
+  Capital = exp(LoggedProductivity)*Capital(-1)^alpha+(1-delta)*Capital(-1)-Consumption;
+  LoggedProductivity = rho*LoggedProductivity(-1)+LoggedProductivityInnovation;
+end;
+
+initval;
+  LoggedProductivityInnovation = 0;
+end;
+
+steady;
+
+endval;
+  LoggedProductivityInnovation = 0.6;
+end;
+
+endval(learnt_in = 5);
+  LoggedProductivityInnovation = 1;
+end;
+
+shocks(learnt_in = 3);
+  var LoggedProductivityInnovation;
+  periods 4;
+  values 0.2;
+end;
+

tests/deterministic_simulations/pfwee_homotopy_linearization.mod

 /* Example that triggers homotopy in perfect foresight simulation with
    expectation errors, and tests linearization. */
 
-var Consumption, Capital, LoggedProductivity;
-
-varexo LoggedProductivityInnovation;
-
-parameters beta, alpha, delta, rho;
-
-beta = .985;
-alpha = 1/3;
-delta = alpha/10;
-rho = .9;
-
-model;
-  1/Consumption = beta/Consumption(1)*(alpha*exp(LoggedProductivity(1))*Capital^(alpha-1)+1-delta);
-  Capital = exp(LoggedProductivity)*Capital(-1)^alpha+(1-delta)*Capital(-1)-Consumption;
-  LoggedProductivity = rho*LoggedProductivity(-1)+LoggedProductivityInnovation;
-end;
-
-initval;
-  LoggedProductivityInnovation = 0;
-end;
-
-steady;
-
-endval;
-  LoggedProductivityInnovation = 0.6;
-end;
-
-endval(learnt_in = 5);
-  LoggedProductivityInnovation = 1;
-end;
+@#include "pfwee_homotopy.inc"
 
 perfect_foresight_with_expectation_errors_setup(periods=200);
 perfect_foresight_with_expectation_errors_solver(homotopy_max_completion_share = 0.8, homotopy_linearization_fallback, steady_solve_algo = 13);

tests/deterministic_simulations/pfwee_homotopy_linearization_condpaths.mod

+/* Example that triggers homotopy in perfect foresight simulation with
+   expectation errors + controlled paths, and tests linearization. */
+
+@#include "pfwee_homotopy.inc"
+
+perfect_foresight_controlled_paths(learnt_in = 7);
+  exogenize LoggedProductivity;
+  periods 8:9;
+  values 5;
+  endogenize LoggedProductivityInnovation;
+end;
+
+perfect_foresight_with_expectation_errors_setup(periods=200);
+perfect_foresight_with_expectation_errors_solver(homotopy_max_completion_share = 0.8, homotopy_linearization_fallback, steady_solve_algo = 13);
+
+if ~oo_.deterministic_simulation.status
+   error('Perfect foresight simulation failed')
+end
+
+if ~(all(abs(oo_.endo_simul(3, 9:10) - 5) < 1e-15) && abs(oo_.exo_simul(10) - 0.5) < 1e-15)
+   error('Homotopy with linearization and controlled paths failed')
+end

tests/deterministic_simulations/pfwee_homotopy_marginal_linearization.mod

 /* Example that triggers homotopy in perfect foresight simulation with
    expectation errors, and tests marginal linearization. */
 
-var Consumption, Capital, LoggedProductivity;
-
-varexo LoggedProductivityInnovation;
-
-parameters beta, alpha, delta, rho;
-
-beta = .985;
-alpha = 1/3;
-delta = alpha/10;
-rho = .9;
-
-model;
-  1/Consumption = beta/Consumption(1)*(alpha*exp(LoggedProductivity(1))*Capital^(alpha-1)+1-delta);
-  Capital = exp(LoggedProductivity)*Capital(-1)^alpha+(1-delta)*Capital(-1)-Consumption;
-  LoggedProductivity = rho*LoggedProductivity(-1)+LoggedProductivityInnovation;
-end;
-
-initval;
-  LoggedProductivityInnovation = 0;
-end;
-
-steady;
-
-endval;
-  LoggedProductivityInnovation = 0.6;
-end;
-
-endval(learnt_in = 5);
-  LoggedProductivityInnovation = 1;
-end;
+@#include "pfwee_homotopy.inc"
 
 perfect_foresight_with_expectation_errors_setup(periods=200);
 perfect_foresight_with_expectation_errors_solver(homotopy_max_completion_share = 0.8, homotopy_marginal_linearization_fallback, steady_solve_algo = 13);

tests/deterministic_simulations/pfwee_homotopy_marglin_condpaths.mod

+/* Example that triggers homotopy in perfect foresight simulation with
+   expectation errors + controlled paths, and tests marginal linearization. */
+
+@#include "pfwee_homotopy.inc"
+
+perfect_foresight_controlled_paths(learnt_in = 7);
+  exogenize LoggedProductivity;
+  periods 8:9;
+  values 5;
+  endogenize LoggedProductivityInnovation;
+end;
+
+perfect_foresight_with_expectation_errors_setup(periods=200);
+perfect_foresight_with_expectation_errors_solver(homotopy_max_completion_share = 0.8, homotopy_marginal_linearization_fallback, steady_solve_algo = 13);
+
+if ~oo_.deterministic_simulation.status
+   error('Perfect foresight simulation failed')
+end
+
+if ~(all(abs(oo_.endo_simul(3, 9:10) - 5) < 1e-14) && abs(oo_.exo_simul(10) - 0.5) < 1e-14)
+   error('Homotopy with marginal linearization and controlled paths failed')
+end

tests/deterministic_simulations/pfwee_learnt_in.inc

+/* Tests perfect_foresight_with_expectation_errors_{setup,solver}
+   using the shocks(learnt_in=…), mshocks(learnt_in=…) and endval(learnt_in=…) syntax
+   with dates (instead of integer indices) for periods */
+
+var c k;
+varexo x;
+
+parameters alph gam delt bet aa;
+alph=0.5;
+gam=0.5;
+delt=0.02;
+bet=0.05;
+aa=0.5;
+
+
+model;
+c + k - aa*x*k(-1)^alph - (1-delt)*k(-1);
+c^(-gam) - (1+bet)^(-1)*(aa*alph*x(+1)*k^(alph-1) + 1 - delt)*c(+1)^(-gam);
+end;
+
+initval;
+x = 1;
+k = ((delt+bet)/(1.0*aa*alph))^(1/(alph-1));
+c = aa*k^alph-delt*k;
+end;
+
+steady;
+
+check;
+
+// p contains period indices, either as integer or as dates
+@#if dates
+@# define p = ["2018Y", "2019Y", "2020Y", "2021Y", "2022Y", "2023Y", "2024Y", "2025Y" ]
+@#else
+@# define p = 1:8
+@#endif
+
+@#if bare_first_info_period
+shocks;
+@#else
+shocks(learnt_in = @{p[1]});
+@#endif
+  var x;
+  periods @{p[1]}:@{p[2]};
+  values 1.2;
+end;
+
+@#if bare_first_info_period
+endval;
+@#else
+endval(learnt_in = @{p[1]});
+@#endif
+  x = 1.05;
+end;
+
+shocks(learnt_in = @{p[2]});
+  var x;
+  periods @{p[2]};
+  add 0.1;
+end;
+
+endval(learnt_in = @{p[2]});
+  x = 1.1;
+end;
+
+shocks(learnt_in = @{p[3]});
+  var x;
+  periods @{p[3]};
+  values 1.4;
+end;
+
+mshocks(learnt_in = @{p[3]});
+  var x;
+  periods @{p[7]};
+  values (1.5/1.2); // 1.2 is the terminal steady as anticipated in period 3
+end;
+
+endval(learnt_in = @{p[3]});
+  x += 0.1;
+end;
+
+// Dummy block, that will be overwritten by the next one
+shocks(learnt_in = @{p[6]});
+  var x;
+  periods @{p[6]}:@{p[8]};
+  values 10;
+end;
+
+shocks(learnt_in = @{p[6]}, overwrite);
+  var x;
+  periods @{p[6]}:@{p[7]};
+  multiply 0.8;
+end;
+
+endval(learnt_in = @{p[6]});
+  x *= 0.75;
+end;
+
+// Save initial steady state (it will be modified by pfwee)
+orig_steady_state = oo_.steady_state;
+orig_exo_steady_state = oo_.exo_steady_state;
+
+perfect_foresight_with_expectation_errors_setup(
+@#if dates
+first_simulation_period = 2018Y, last_simulation_period = 2024Y
+@#else
+periods = 7
+@#endif
+);
+
+perfect_foresight_with_expectation_errors_solver;
+pfwee_simul = oo_.endo_simul;
+
+// Now compute the solution by hand to verify the results
+verbatim;
+
+oo_.steady_state = orig_steady_state;
+oo_.exo_steady_state = orig_exo_steady_state;
+
+oo_ = make_ex_(M_,options_,oo_);
+oo_ = make_y_(M_,options_,oo_);
+
+@#if dates
+% Reset {first,last}_simulation_period since we’re going to manipulate options_.periods
+options_.simul.first_simulation_period = dates();
+options_.simul.last_simulation_period = dates();
+options_.periods = 7;
+@#endif
+
+% Information arriving in period 1 (temp shock now and tomorrow + permanent shock in future)
+oo_.exo_simul(2:3,1) = 1.2;
+oo_.exo_simul(4:end,1) = 1.05;
+oo_.exo_steady_state = 1.05;
+oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_, true);
+oo_.endo_simul(:, end) = oo_.steady_state;
+oo_=perfect_foresight_solver(M_, options_, oo_);
+
+% Information arriving in period 2 (temp shock now + permanent shock in future)
+oo_.exo_simul(3,1) = 1.3;
+oo_.exo_steady_state = 1.1;
+oo_.exo_simul(4:end, 1) = oo_.exo_steady_state;
+oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_, true);
+oo_.endo_simul(:, end) = oo_.steady_state;
+options_.periods = 6;
+saved_endo = oo_.endo_simul(:, 1);
+saved_exo = oo_.exo_simul(1, :);
+oo_.endo_simul = oo_.endo_simul(:, 2:end);
+oo_.exo_simul = oo_.exo_simul(2:end, :);
+oo_=perfect_foresight_solver(M_, options_, oo_);
+oo_.endo_simul = [ saved_endo oo_.endo_simul ];
+oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
+
+% Information arriving in period 3 (temp shocks + permanent shock in future)
+oo_.exo_simul(4,1) = 1.4;
+oo_.exo_steady_state = 1.1+0.1;
+oo_.exo_simul(8,1) = (1.5/1.2)*oo_.exo_steady_state;
+oo_.exo_simul(5:7, 1) = oo_.exo_steady_state;
+oo_.exo_simul(9:end, 1) = oo_.exo_steady_state;
+oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_, true);
+oo_.endo_simul(:, end) = oo_.steady_state;
+options_.periods = 5;
+saved_endo = oo_.endo_simul(:, 1:2);
+saved_exo = oo_.exo_simul(1:2, :);
+oo_.endo_simul = oo_.endo_simul(:, 3:end);
+oo_.exo_simul = oo_.exo_simul(3:end, :);
+oo_=perfect_foresight_solver(M_, options_, oo_);
+oo_.endo_simul = [ saved_endo oo_.endo_simul ];
+oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
+
+% Information arriving in period 6 (temp shocks + permanent shock)
+oo_.exo_simul(7,1) = (1.1+0.1)*0.8;
+oo_.exo_simul(8,1) = 1.5*0.8;
+oo_.exo_steady_state = (1.1+0.1)*0.75;
+oo_.exo_simul(9:end, 1) = oo_.exo_steady_state;
+oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_, true);
+oo_.endo_simul(:, end) = oo_.steady_state;
+options_.periods = 2;
+saved_endo = oo_.endo_simul(:, 1:5);
+saved_exo = oo_.exo_simul(1:5, :);
+oo_.endo_simul = oo_.endo_simul(:, 6:end);
+oo_.exo_simul = oo_.exo_simul(6:end, :);
+oo_=perfect_foresight_solver(M_, options_, oo_);
+oo_.endo_simul = [ saved_endo oo_.endo_simul ];
+oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
+
+% We should have strict equality with first pfwee simulation, because algorithm
+% and guess values are exactly the same.
+if any(any(pfwee_simul-oo_.endo_simul ~= 0))
+    error('Error in perfect_foresight_with_expectation_errors')
+end
+
+@#if dates
+if ~isequal(Simulated_time_series.dates, dates('2017Y'):dates('2025Y'))
+    error('Incorrect Simulated_time_series object')
+end
+@#endif
+
+end; // verbatim

tests/deterministic_simulations/pfwee_learnt_in.mod

 /* Tests perfect_foresight_with_expectation_errors_{setup,solver}
    using the shocks(learnt_in=…), mshocks(learnt_in=…) and endval(learnt_in=…) syntax */
 
-var c k;
-varexo x;
+@#define bare_first_info_period = false
+@#define dates = false
 
-parameters alph gam delt bet aa;
-alph=0.5;
-gam=0.5;
-delt=0.02;
-bet=0.05;
-aa=0.5;
-
-
-model;
-c + k - aa*x*k(-1)^alph - (1-delt)*k(-1);
-c^(-gam) - (1+bet)^(-1)*(aa*alph*x(+1)*k^(alph-1) + 1 - delt)*c(+1)^(-gam);
-end;
-
-initval;
-x = 1;
-k = ((delt+bet)/(1.0*aa*alph))^(1/(alph-1));
-c = aa*k^alph-delt*k;
-end;
-
-steady;
-
-check;
-
-shocks(learnt_in = 1);
-  var x;
-  periods 1:2;
-  values 1.2;
-end;
-
-shocks(learnt_in = 2);
-  var x;
-  periods 2;
-  add 0.1;
-end;
-
-endval(learnt_in = 2);
-  x = 1.1;
-end;
-
-shocks(learnt_in = 3);
-  var x;
-  periods 3;
-  values 1.4;
-end;
-
-mshocks(learnt_in = 3);
-  var x;
-  periods 7;
-  values (1.5/1.2); // 1.2 is the terminal steady as anticipated in period 3
-end;
-
-endval(learnt_in = 3);
-  x += 0.1;
-end;
-
-// Dummy block, that will be overwritten by the next one
-shocks(learnt_in = 6);
-  var x;
-  periods 6:8;
-  values 10;
-end;
-
-shocks(learnt_in = 6, overwrite);
-  var x;
-  periods 6:7;
-  multiply 0.8;
-end;
-
-endval(learnt_in = 6);
-  x *= 0.75;
-end;
-
-// Save initial steady state (it will be modified by pfwee)
-orig_steady_state = oo_.steady_state;
-orig_exo_steady_state = oo_.exo_steady_state;
-
-perfect_foresight_with_expectation_errors_setup(periods = 7);
-
-perfect_foresight_with_expectation_errors_solver;
-pfwee_simul = oo_.endo_simul;
-
-// Now compute the solution by hand to verify the results
-oo_.steady_state = orig_steady_state;
-oo_.exo_steady_state = orig_exo_steady_state;
-
-perfect_foresight_setup;
-
-verbatim;
-
-% Information arriving in period 1 (temp shock now and tomorrow)
-oo_.exo_simul(2:3,1) = 1.2;
-oo_=perfect_foresight_solver(M_, options_, oo_, true);
-
-% Information arriving in period 2 (temp shock now + permanent shock in future)
-oo_.exo_simul(3,1) = 1.3;
-oo_.exo_steady_state = 1.1;
-oo_.exo_simul(4:end, 1) = oo_.exo_steady_state;
-oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_, true);
-oo_.endo_simul(:, end) = oo_.steady_state;
-options_.periods = 6;
-saved_endo = oo_.endo_simul(:, 1);
-saved_exo = oo_.exo_simul(1, :);
-oo_.endo_simul = oo_.endo_simul(:, 2:end);
-oo_.exo_simul = oo_.exo_simul(2:end, :);
-oo_=perfect_foresight_solver(M_, options_, oo_, true);
-oo_.endo_simul = [ saved_endo oo_.endo_simul ];
-oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
-
-% Information arriving in period 3 (temp shocks + permanent shock in future)
-oo_.exo_simul(4,1) = 1.4;
-oo_.exo_steady_state = 1.1+0.1;
-oo_.exo_simul(8,1) = (1.5/1.2)*oo_.exo_steady_state;
-oo_.exo_simul(5:7, 1) = oo_.exo_steady_state;
-oo_.exo_simul(9:end, 1) = oo_.exo_steady_state;
-oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_, true);
-oo_.endo_simul(:, end) = oo_.steady_state;
-options_.periods = 5;
-saved_endo = oo_.endo_simul(:, 1:2);
-saved_exo = oo_.exo_simul(1:2, :);
-oo_.endo_simul = oo_.endo_simul(:, 3:end);
-oo_.exo_simul = oo_.exo_simul(3:end, :);
-oo_=perfect_foresight_solver(M_, options_, oo_, true);
-oo_.endo_simul = [ saved_endo oo_.endo_simul ];
-oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
-
-% Information arriving in period 6 (temp shocks + permanent shock)
-oo_.exo_simul(7,1) = (1.1+0.1)*0.8;
-oo_.exo_simul(8,1) = 1.5*0.8;
-oo_.exo_steady_state = (1.1+0.1)*0.75;
-oo_.exo_simul(9:end, 1) = oo_.exo_steady_state;
-oo_.steady_state = evaluate_steady_state(oo_.steady_state, oo_.exo_steady_state, M_, options_, true);
-oo_.endo_simul(:, end) = oo_.steady_state;
-options_.periods = 2;
-saved_endo = oo_.endo_simul(:, 1:5);
-saved_exo = oo_.exo_simul(1:5, :);
-oo_.endo_simul = oo_.endo_simul(:, 6:end);
-oo_.exo_simul = oo_.exo_simul(6:end, :);
-oo_=perfect_foresight_solver(M_, options_, oo_, true);
-oo_.endo_simul = [ saved_endo oo_.endo_simul ];
-oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
-
-% We should have strict equality with first pfwee simulation, because algorithm
-% and guess values are exactly the same.
-if any(any(pfwee_simul-oo_.endo_simul ~= 0))
-    error('Error in perfect_foresight_with_expectation_errors')
-end
-
-end; // verbatim
+@#include "pfwee_learnt_in.inc"

tests/deterministic_simulations/pfwee_learnt_in_bare_1st_period.mod

+/* Tests perfect_foresight_with_expectation_errors_{setup,solver}
+   using the shocks(learnt_in=…), mshocks(learnt_in=…) and endval(learnt_in=…) syntax,
+   except for the first informational period where the learnt_in keyword is omitted */
+
+@#define bare_first_info_period = true
+@#define dates = false
+
+@#include "pfwee_learnt_in.inc"

tests/deterministic_simulations/pfwee_learnt_in_dseries.mod

+/* Tests perfect_foresight_with_expectation_errors_{setup,solver}
+   using the shocks(learnt_in=…), mshocks(learnt_in=…) and endval(learnt_in=…) syntax
+   with dates (instead of integer indices) for periods */
+
+@#define bare_first_info_period = false
+@#define dates = true
+
+@#include "pfwee_learnt_in.inc"