Log linearization


I am new to DSGE, and I need to replicate the paper “Fiscal Policy, Wealth Effects, and Markups” (Monacelli and Perottiz). They used log-linearized model to calculate their impulse response function, but they didn’t write out the equations after log linearization. I don’t know how to log linearize the model and want to avoid it. However there are some parameter values the authors didn’t give, I guess they eliminated these parameters through log linearization. If I don’t want to do log linearization manually, how to deal with these parameters ?

Thanks in advance.

If you are not comfortable with log-linearization, do not attempt this replication. You will fail.

Thanks for your reply. Are there any good materials covering this kind of log linearization? I have read some materials e.g. The abc of RBC, but I still don’t know how to linearize this model.
I also have devoted much time in collecting data of this paper, so I don’t want to give it up.
Many thanks.

The standard reference for this setup would be Gali’s textbook.

A related question about log linearization.

If I want to create the impulse response of log deviation of the variables. The standard way is to let dynare log-linearize the model and create impulse response. But I could also let dynare linearize the model. In the model, I define new variables as log of the variables. Then I create impulse response based on the linearized model. Are the two impulse responses equivalent?


When you define an additional variable

the IRF to this variable will be equivalent to a full log-linearization in y.

Thank you!