Hi,
I have a question about understanding irfs in my model. I’ve seen several posts on the forum about it but I’m still confusing.
My question is: in my model, does the vertical axis in irfs’ graph represent percentage deviation from steady state value or just the difference b/w variable following a shock and its steady state value? For example, does y in irf graph represent (yt-y*)/y* or yt-y*, where yt is output following a shock and y* is the steady-state output ?

Hi, It depends on the approximation. The default is linearization, in this case the IRFs are in deviations to the deterministic steady state. At first order you can use the option loglinear, Dynare log linearize the model and the IRFs are in percentage deviations to the deterministic steady state.

To be more precise: it depends on how the variables in your model are measured. As @stepan-a says, Dynare conducts a linearization.

If you for example have a nonlinear model and your model variable is simply the level of output, the IRF will measure the difference between the variable and its steady state (or its mean at higher order). This is the case with https://github.com/DynareTeam/dynare/blob/master/examples/example1.mod

In contrast, if your model variable already measures percentages, you will get the percentage deviation from the steady state, because that is what the simple deviation from steady state measures in this case. This is for example the case when you entered an already log-linearized model where all variables are percentage deviations from trend.

Now there are cases where in a nonlinear model you want percentage deviations from steady state instead of simple deviations. There are three ways to do this:

Hello @jpfeifer !
Following up on this old thread: I just wanted to understand how to set the value of eps_a to get a 1 percentage deviation from the steady state in inflation on impact. If you could please elaborate on this for the code of Gali chapter 3 linear file? Thank you!

I was just wondering about how to enter percentage shocks in a non linear model so when Dynare does the linearization, the shock will be to a 1% increase in the shocked variable.

One way to do it is by introducing the shock using exponential and solving the model at order 1:

Y = exp(A)*K(-1)^alpha
A = rho*A(-1) + e

If we fix the stderr of e to 1, then we have a 1% shock over A right?

But do we obtain the same if one define the shock in a log like this ?

Y = A*K(-1)^alpha
log(A) = rho*log(A(-1)) + e

Mathematically the expressions are equivalent with a transformation, but I’m wondering if Dynare will also interpret this as a 1% increase in A when using the order=1 command and not the loglinear one.