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Removing debugging code for extended path. Updating test cases.

time-shift
Michel Juillard 2012-06-01 15:00:56 +02:00
parent dc7c0fa74d
commit ec05f302b7
5 changed files with 31 additions and 10 deletions
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4
matlab/solve_stochastic_perfect_foresight_model.m
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@ -193,8 +193,8 @@ function [flag,endo_simul,err] = solve_stochastic_perfect_foresight_model(endo_s
end
flag = 0;% Convergency obtained.
endo_simul = reshape(Y(:,1),ny,periods+2);%Y(ny+(1:ny),1);
figure;plot(Y(16:ny:(periods+2)*ny,:))
pause
% figure;plot(Y(16:ny:(periods+2)*ny,:))
% pause
break
end
dy = -A\res;

1
tests/Makefile.am
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@ -126,6 +126,7 @@ MODFILES = \
second_order/ds1.mod \
second_order/ds2.mod \
ep/rbc.mod \
ep/rbcii.mod \
ep/linear.mod \
deterministic_simulations/deterministic_model_purely_forward.mod \
deterministic_simulations/rbc_det1.mod \

5
tests/ep/mean_preserving_spread.m
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@ -1,4 +1,4 @@
function m = mean_preserving_spread(autoregressive_parameter)
function m = mean_preserving_spread(autoregressive_parameter,sigma)
% Computes the mean preserving spread for first order autoregressive process.
%
% The mean preserving spread m is a constant such that the mean of the process
@ -13,6 +13,5 @@ function m = mean_preserving_spread(autoregressive_parameter)
% AUTHOR(S)
% stephane DOT adjemian AT univ DASH lemans DOT fr
% frederic DOT karame AT univ DASH evry DOT fr
global M_
m = M_.Sigma_e/(1-autoregressive_parameter*autoregressive_parameter);
m = sigma/(1-autoregressive_parameter*autoregressive_parameter);

4
tests/ep/rbc.mod
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@ -57,10 +57,10 @@ options_.ep.stochastic.nodes = 2;
options_.console_mode = 0;
options_.ep.stochastic.order = 0;
ts0 = extended_path([],100);
//ts0 = extended_path([],100);
options_.ep.stochastic.order = 1;
ts1 = extended_path([],100);
//ts1 = extended_path([],100);
options_.ep.stochastic.order = 2;
ts2 = extended_path([],100);

27
tests/ep/rbcii.mod
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@ -26,15 +26,15 @@ sigma2 = 0.001;
rho = 0.800;
@#endif
external_function(name=mean_preserving_spread);
external_function(name=mean_preserving_spread,nargs=2);
model(use_dll);
model;
// Eq. n°1:
efficiency = rho*efficiency(-1) + EfficiencyInnovation;
// Eq. n°2:
Efficiency = effstar*exp(efficiency-mean_preserving_spread(rho));
Efficiency = effstar*exp(efficiency-mean_preserving_spread(rho,sigma2));
// Eq. n°3:
Output = Efficiency*(alpha*(Capital(-1)^psi)+(1-alpha)*(Labour^psi))^(1/psi);
@ -56,6 +56,27 @@ model(use_dll);
end;
steady_state_model;
efficiency = 0;
Efficiency = effstar*exp(efficiency-mean_preserving_spread(rho,sigma2));
// 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;
Investment = delta*Capital;
ExpectedTerm = beta*((((Consumption^theta)*((1-Labour)^(1-theta)))^(1-tau))/Consumption)*(alpha*((Output/Capital)^(1-psi))+1-delta);
end;
@#if extended_path_version