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
parent 43311a23
Pipeline #4256 passed with stages
in 64 minutes and 54 seconds
......@@ -4240,7 +4240,9 @@ 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
......
......@@ -40,46 +40,39 @@ if debug
save ep_test_1.mat endo_simul exo_simul
end
if bytecode_flag && ~ep.stochastic.order
try
tmp = bytecode('dynamic', endo_simul, exo_simul, M.params, endo_simul, periods);
flag = false;
catch ME
disp(ME.message);
flag = true;
end
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
......
......@@ -289,6 +289,7 @@ 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 \
......
// 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);
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