# 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 don’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).