I have a short question. Thank you for your reply.
can we say which linear model is better or nonlinear model? or does it depend on the features of the model? For example, the size of the model and so on.
For example, in a nonlinear model, we have to set the steady state of the model accurately. And this is difficult in large models.
whether it can be said that the nonlinear model gives less error and gives better results or not?
can it be recommended to work with a nonlinear or linear model? or it does not matter
I don’t understand the question. Linear models usually do require the computation of the steady state as well. How would you otherwise linearize around this point. There are only a few exceptions where you can transform the steady state levels used in the linearized model to ratios.
Thank you, Professor. My question is, if we compute the steady state in both cases, what is the need to linearize the model and devote time to work and effort. Does the linear model have the advantage of doing this? Does that mean dynare works better with a linear model or does it not matter?