Fix extended_path with bytecode

By the way, document and enforce the fact that stochastic extended_path (i.e.
order > 0) is not compatible with either bytecode or block.

Closes: #1742

(manually cherry picked from commit 71e3e0d4)

doc/manual/source/the-model-file.rst

@@ -3732,7 +3732,9 @@ which is described below.
        the endogenous variables are generated by assuming that the
        agents believe that there will no more shocks after period
        :math:`t+S`. This is an experimental feature and can be quite
-       slow. Default: ``0``.
+       slow. A non-zero value is not compatible with either the
+       ``bytecode`` or the ``block`` option of the ``model`` block.
+       Default: ``0``.
 
     .. option:: hybrid
 

matlab/ep/extended_path_core.m

@@ -40,40 +40,39 @@ if debug
     save ep_test_1.mat endo_simul exo_simul
 end
 
-if bytecode_flag && ~ep.stochastic.order
-    [flag, tmp] = bytecode('dynamic', endo_simul, exo_simul, M.params, endo_simul, periods);
-else
-    flag = true;
+if bytecode_flag && order > 0
+    error('Option order > 0 of extended_path command is not compatible with bytecode option.')
+end
+if options.block && order > 0
+    error('Option order > 0 of extended_path command is not compatible with block option.')
 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.solve_algo = solve_algo;
-        options.stack_solve_algo = stack_solve_algo;
-        tmp = perfect_foresight_solver_core(M, options, oo);
-        if ~tmp.deterministic_simulation.status
-            info_convergence = false;
-        else
-            info_convergence = true;
-        end
+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.solve_algo = solve_algo;
+    options.stack_solve_algo = stack_solve_algo;
+    tmp = perfect_foresight_solver_core(M, options, oo);
+    if ~tmp.deterministic_simulation.status
+        info_convergence = false;
     else
-        switch(algo)
-          case 0
+        info_convergence = true;
+    end
+else
+    switch(algo)
+        case 0
             [flag, tmp.endo_simul] = ...
-                solve_stochastic_perfect_foresight_model(endo_simul, exo_simul, pfm, ep.stochastic.quadrature.nodes, ep.stochastic.order);
-          case 1
+            solve_stochastic_perfect_foresight_model(endo_simul, exo_simul, pfm, ep.stochastic.quadrature.nodes, ep.stochastic.order);
+        case 1
             [flag, tmp.endo_simul] = ...
-                solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options, pfm, ep.stochastic.order);
-        end
-        info_convergence = ~flag;
+            solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options, pfm, ep.stochastic.order);
     end
+    info_convergence = ~flag;
 end
 
 if ~info_convergence && ~options.no_homotopy
@@ -86,4 +85,4 @@ else
     y = NaN(size(endo_nbr,1));
 end
 
-endogenousvariablespaths = tmp.endo_simul;
\ No newline at end of file
+endogenousvariablespaths = tmp.endo_simul;

tests/Makefile.am

@@ -281,6 +281,7 @@ MODFILES = \
 	ep/rbcii.mod \
 	ep/linearmodel0.mod \
 	ep/linearmodel1.mod \
+	ep/rbc_bytecode.mod \
 	stochastic_simulations/example1_noprint.mod \
 	stochastic-backward-models/solow_cd.mod \
 	stochastic-backward-models/solow_ces.mod \

tests/ep/rbc_bytecode.mod

+// Test extended_path + bytecode (regression check for #1742)
+
+var Capital, Output, Labour, Consumption, Efficiency, efficiency, ExpectedTerm;
+
+varexo EfficiencyInnovation;
+
+parameters beta, theta, tau, alpha, psi, delta, rho, effstar, sigma;
+
+/*
+** Calibration
+*/
+
+
+beta    =  0.990;
+theta   =  0.357;
+tau     =  30.000;
+alpha   =  0.450;
+psi     =  -5.000;
+delta   =  0.020;
+rho     =  0.950;
+effstar =  1.000;
+sigma   =  0.010;
+
+model(bytecode);
+
+  // Eq. n°1:
+  efficiency = rho*efficiency(-1) + sigma*EfficiencyInnovation;
+
+  // Eq. n°2:
+  Efficiency = effstar*exp(efficiency-.5*sigma*sigma/(1-rho*rho));
+
+  // Eq. n°3:
+  Output = Efficiency*(alpha*(Capital(-1)^psi)+(1-alpha)*(Labour^psi))^(1/psi);
+
+  // Eq. n°4:
+  Consumption + Capital - Output - (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 = 0;
+Efficiency = effstar;
+// Compute steady state ratios.
+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;
+
+
+shocks;
+var EfficiencyInnovation = 1;
+end;
+
+steady(nocheck);
+
+extended_path(periods=10, order=0);