i’m trying to replicate Cúrdia et al (2015), but my estimated parameters differ slightly to the same data. I suspect the problem is that the steady state is not being updated with each new value for estimated parameters.
The model is log-linearized, so all variables have a value of 0 in steady state, with the exception of observables. The point is to update the observables with the updated parameters in estimation. How can i do this in a simple way, i.e., there is no need for numerical solving for steady state values, i just want to update the observables.
- The newly estimated parameters that appears in the observation equations and in steady state equations arent updated at all. The value assigned to the steady state (when estimation is complete) is simply the associated calibrated value.
- I’ve tried to work with the steadystate file examples but they solve it numerically. I dont know how to create a simple structure that just assigns “zero” to the model variables and the associated parameters value to the observable. In my case the observables are:
- gdp growth in steady state = gamma (average growth rate of productivity process);
- inflation in steady state = pistar (long run inflation target);
- ffr in steady state = r (steady state real interest rate) + pistar;
gamma, pistar and r are parameters to be estimated, so its clear that they need to be updated in steady state computations.