Dear all,
I am simulating a DSGE model that is represented by a system of nonlinear expectational difference equations using Dynare. A solution to such a system is called a set of policy functions which are approximated by using perturbation techniques. According to theory, a policy function maps state variables of the model into choices of the control variables. Is this reasoning correct?
As an example, I get the following policy function for the control variable L (represents labor demand):
L = 70 + ... + 3.81 \tau_{t-1}
\tau_{t-1} represents a tax rate and takes on values between 0 and 1. Do I interpret the coefficient of 3.81 in a similar way as if the policy function was simply a linear regression setup? And does the interpretation of the coefficient depend on the scaling of \tau_{t-1}? So if I were to increase the tax rate from t-1 by 1 percentage point, by how much does L change?
Thank you!
Best, Matthias