Stochastic steady state and deterministic steady state


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


  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;.