Dear all,

To investigate problems in a model, it was suggested that one give the two commands

estimation (where you put mode_compute=0, mh_replic=1, order=1 and provide a dataset)

prior simulate;

The model is good if the (effective) prior mass is close to 1.

Below is what I found for my model:

negative values are not allowed

Prior mass = 0.023

BK indeterminacy share = 0

BK unstability share = 0

BK singularity share = 0

Complex jacobian share = 0

mjdgges crash share = 0

Steady state problem share = 0

Complex steady state share = 0

Analytical steady state problem share = 0.977

Total computing time : 4h07m19s

Note: warning(s) encountered in MATLAB/Octave code

I have loaded the 4x4 matrix “modelname_results.mat” generated after estimation

load(‘January2018cmrV3_results.mat’)

view

ans =

```
1.0000 0 0 -0.5000
0 1.0000 0 -0.5000
0 0 -1.0000 9.1603
0 0 0 1.0000
```

My questions are:

- Is there any link between the values of the elements of that results matrix and my endogenous variables?
- I have noticed that the risk premium term coefficients are very influential in the model. If I calibrate one of them the “negative value not allowed” error shows at the Identification step. If I estimate both parameters, the “negative value not allowed” error shows later in the Estimation step. Is there a way to see which of the endogenous turns negative in the estimation process to help fix the model?
- Suppressing altogether the shock on the risk premium give “the matrix must be positive definite” message error.

I would really appreciate if someone could give a hint in all those problems, may be even proceed to have a fresh look at my model because I feel like I am half way home.

Thank you for your time.