Stochastic steady state and deterministic steady state

Hi,

I build a New Keynesian model with a frictional labor market and I am interested by the simulation of a second moment shock (uncertainty). To do this, after the use of the stoch_simul function, I use the function simult_ in order to compute the" stochastic steady state" of the model with no shock.

The problem is that the" stochastic steady state" computed by the simult_ is too far compared to the “deterministic steady state”. For example, in my model I have a job finding rate with a steady state equal to 0.53. When the simult_ function compute its stochastic counterpart it finds a value equal to 1.37. This value is problematic since by definition a probability should be lesser than 1.

My questions are as follows:
1. Is it possible to restrict the value of the stochastic steady state to be close to the deterministic steady state?
2. What could be the origin of my problem?

Thaks a lot for your help

:wink:

  1. Restrictions are not possible, because the stochastic steady state is an endogenous object.
  2. The difference between the deterministic and the stochastic steady state come from a precautionary motive of the agents in the model. It is a combination of the curvature of the model and the amount of risk in the model. Maybe there is too much risk in your model. Did you keep in mind that a shock size of 1% must be stderr 0.01; and not stderr 1;.