# Mixing shocks

Dear All,

I would like to mix deterministic and stochastic shock (i.e., the model is stochastic, but agents know from the start of the simulation about future exogenous changes).

This is how I try to do it:

var k, c, y, z;
varexo_det e;
varexo u;
parameters rho psi alpha;
parameters alpha, beta, delta, rho;

alpha = 0.36;
beta = 0.98;
delta = 0.2;
rho = 1;

model;
1/c = beta * 1/c(+1) * (exp(z(+1)) * alpha * k^(alpha-1) + 1 - delta);
y = exp(e)*exp(z) * k(-1)^alpha;
k = y - c + (1 - delta) * k(-1);
z = rho * z(-1) + u;
end;

initval;
k = 0.1;
c = 1;
y = 1;
z = 0;
u = 0;
end;
shocks;
var u; stderr 0.05;
var e;
periods 1:1 2:2 3:3;
values 0.01 0.02 0.03;
end;
stoch_simul(irf=20,drop=0);
forecast(periods=200);

Is that correct (I was also thinking about including e=0 in initval, but I am not sure this would be correct)?

If yes, why IRFs for this model are exactly the same as IRFs for the model without a deterministic shock? I would think that steady-states for the two models should differ given that with stoch_simul and deterministic shock I should get IRFs that are conditional on this deterministic shock?

Am I missing something?

Many thanks for all your potential responses.

I have the same problem. When I run the code with and without the deterministic shock, the time series generated by Dynare are the same for both cases!! Anyone knows why that happens?

Exogenous deterministic variables donâ€™t affect the IRFs. But they do affect the forecasts, as you can see from the graphs (you probably need to zoom in on the first periods to see the difference).