Schmitt-Grohe & Uribe (2018) replication

#1

Dear all,
I am trying to replicate Schimitt-Grohe and Uribe (2018) model (article attached), but I was not able to come to the same results as the authors using exactly their data. From my point of view, the only difference I made in the implementation of this model (mod file attached), is that I assumed the observation error to be Normal (0;Var of the series)/10), i.e. I’ve written observations errors as shocks with a given standard error. In the original paper, the authors also estimate the standard error of the estimation errors, but I was not able to do this in dynare as I get the error that you cannot estimate paramaters used only in shocks.

Kindly give your input on this matter. Any advice as to how to optimize the estimation would also be greatly welcomed, as right now a full estimation takes more than two days of continous running.

Best Regards.

w25380.pdf (292.8 KB)
dizertatiev4.mod (10.7 KB)

#2
  1. What happens if you use their reported parameter values as your starting values?
  2. Why do you use mode_compute=6? That is a time-intensive but inefficient optimizer
  3. What exactly is your problem in estimating/specifying the measurement error?
#3

Thank you for the help prof. Pfeifer.
To answer your questions:
1.What do you mean by reported parameter values?
2.It’s my first experience with Dynare and that’s the optimizer I’ve seen used in an example.:smile: I’ll try a different type.
3. If I declare the members of R matrix as parameters, include them in the estimation, and write a shock like var miu1; stderr r1, I get the

ERROR: some estimated parameters (r1, r2, r3, r4, r5) also appear in the expressions defining the variance/covariance matrix of shocks; this is not allowed.

I’ve been trying to write the model more explicitly, as indicated in the annex. I still omit the unconditional expectations because I don’t know how to write that in dynare code. Unfortunately, I’m having trouble finding a steady state. I attached the mod file for two lags (the authors use six).

Best Regards

dizertatievtrend_2.mod (6.0 KB)

#4
  1. The question is whether there is simply a problem of mode-finding. If you know the results of the SGU estimation, you could try their values instead of starting at the prior mean.
  2. Please provide the data file
  3. The error message suggests that your implementation here was wrong.
  4. Regarding the steady state, you should use
steady_state_model;
y=0;
pi=0;
i=0;
e=0;
if=0;
x=0;
xm=0;
xmf=0;
zm=0;
z=0;
yobs=a1;
robs=a2;
iobs=a3;
eobs=a4;
ifobs=a5;
end;
  1. If I see it correctly, then the unconditional expectations is just there to denote that the data has been demeaned.