I intend to do an experiment from a calibrated model that consists in simulating how inflation and inflation expectations evolve during 80 periods. I have a vector with a given shock in each of these periods.
I tried to use the EXPECTATION feature in a deterministic model to recover inflation expectations, but the output is just the lagged inflation. As far as I undestood Dynare manual, this is because the agent is supposed to know all the future path of the shocks. Is it possible to recover expectations supposing that the agent does not know the subsequent path of the shocks?
I was wondering if a stochastic simulation is the correct way to do this kind of simulation, but the agent would still know the future shocks trajectory (in this case, zero). Maybe it would work if I am able to inform the shocks vector in a stochastic context, but I also couldn´t find a way to do that.
You need stochastic simulations, because only there expectations formation is non-trivial. Then, you have to ask yourself whether the shock series for your model is perfectly anticipated.
Thank you very much for your last answer, although I am still in trouble to solve my problem. Below, I present the whole problem:
I want to treat the model as if it was the true data generating process (DGP) of economic variables, and assess what an empirical researcher, given outcomes from this DGP, would conclude about the behaviour of inflation expectations. To this end, Monte Carlo simulations will be performed 1000 times. In each of these simulations, a random realization of 80 periods is generated for two different and uncorrelated shocks to the interest rate rule. Then, the model is solved according to this shocks information and inflation rate and its expectations should be stored for each period.
To my mind, it means that economic agents does not perfectly anticipate the shocks.
Could you please provide some information on what dynare features should I use to implement this?