# Writing the policy equation

Hello everyone!

I have some questions but I will only write the first one here:

1. My model is solved and I get all the impulse responses, however, I use the steady states value that I solved ( pi_ss =1, R_ss=1/beta, and I solved for Y_ss) in the monetary policy function in the model as following:

code=((exp(Y)/Y_ss)^rho_Y)*((exp(pi)/pi_ss)^rho_pi)*exp(v);
[/code]
Recently, I found some codes that use the command “STEADY_STATE(variable)” instead of using the steady state value as following

```exp(R - STEADY_STATE(R)) = exp(rho_pi*(pi - STEADY_STATE(pi)) + rho_Y*(Y - STEADY_STATE(Y)) + ev); ```

When I use the second way, I do get impulse responses and the model is solved but when I run the command “model_diagnostic”, the policy equation becomes colinear with almost all the variables… is that a big problem?

One problem I also face with the second way is that when I try solving the model with second order approximations under stochastic shock I face some problem in finding the welfare loss function…

Is my first way of writing the policy function right? or that would be wrong to stick with it. Any suggestions?

Thanks alot for all the help guys!

[code]model_diagnostic: the Jacobian of the static model is singular
there is 1 colinear relationships between the variables and the equations
Colinear variables:
b_star
C
L
lammbda
pn_omega
pd_omega
pf_omega
R
w
k
Z
Y
Yf
Yd
Yx
Yo
pi
pi_d
pi_f
s
q
pd_bar
pf_bar
n1
n2
j1
j2
Sd
Colinear equations
21

The presence of a singularity problem typically indicates that there is one
redundant equation entered in the model block, while another non-redundant equation
is missing. The problem often derives from Walras Law.
Total computing time : 0h00m05s
Note: 2 warning(s) encountered in the preprocessor
EDU>> [/code]

You cannot use the steady_state operator in this case. See the discussion at the bottom of [Open Economy)

So I assume the way I have it written using R_ss, pi_ss, and Y_ss is the right way?

In principle, yes. Just one additional note: If you are using estimation, you need to make sure that parameter dependencies are correctly taken into account via model-local variables or a steady state file.