Rare Disaster: deterministic simulation or stochastic simulation?

Hello everyone!
I want to simulate the dynamic path of economic recovery to the initial steady state after a disaster occurs. Everyone knows that a disaster will happen, but they don’t know when. When a one-time disaster occurs, the variable values in the model will suddenly deviate from the steady state, and we assume that the disaster has no lasting effects, so the economy will gradually recover to the initial steady state. This sounds similar to impulse response, but I’m not sure if it’s the same. Should I use stochastic simulation or deterministic simulation?
Thanks in advance!

Here are my two current attempts.

Attempt1:

//define endogenous variable

var y c k i

//define exogenous variable

Varexo e

//define parameters

parameters

......;

//equilibrium conditions

Model;

model;



​    y = k^alpha * (1-z);



​    k(+1) = (1 - delta) * k + i;

​    c + i = y;



​    c = beta * (y - delta * k);

end;



initval;

​    k = 10;

​    c = 2;

​    i = 0.75 * c;

​    z = 0; 

end;



shocks;

​    var z = 0.5^2;

end;

stoch_simul(order=3, periods=1000, irf=40,nograph);

Attempt 2:

//define endogenous variable

var y c k i

//define exogenous variable

Varexo e

//define parameters

parameters

......;

//equilibrium conditions

Model;

model;



​    y = k^alpha * (1-z);



​    k(+1) = (1 - delta) * k + i;

​    c + i = y;



​    c = beta * (y - delta * k);

end;



initval;

​    k = 10;

​    c = 2;

​    i = 0.75 * c;

​    z = 0; 

end;



shocks;

​    var z;

​    periods 1;

​    values 0.5; 

end;

perfect_foresight_setup(periods = 30);

perfect_foresight_solver;
1 Like

For a surprise shock, there is no difference between stochastic simulations at first order and perfect foresight ones if the model is linear. You need to decide which properties of the two simulation types you are interested in. Do you need stochastics? Do you want full nonlinearity?