[quote=“jpfeifer”]If you are sure that in the respective regions there are no other reasons that the model solution cannot be computed/rejected, then you are fine. What you can do to test this is run the sensitivity command on the model to map the prior region.

Lastly, note that you do not need to provide lower and upper bounds when you are working with the generalized distributions, because they are redundant.[/quote]

Dear Johannes,

Sincerely thank you very much for your taking your much time answering me so many questions on this topic.

There is one issue I suddenly realized and hope to seek help from you again

If I understand correctly ,to make model comparison, prior must integrate to 1, so model solution should be able to be computed in ANYWHERE in the prior region. In other words, to make a comparison between in/determinacy model, if I build a determinacy model with dynare, the prior must Not cover indeterminacy region; if I build a indeterminacy model (following Farmer JEDC 2015), the prior must Not cover determinacy region.

The issue is that I can **Never find VERY EXACT** bound between determinacy and indeterminacy. For example, I know when 1<theta<=1.355 the model is determinate, while theta >=1.3556 the model is indeterminate. So,the region 1.335<theta<1.336 must cover both indeterminacy and determinacy.

Under such circumstance , I am wondering if I can still make model comparison to see if the data favours indeterminacy or determinacy model, I can think of two cases to deal with it, but neither of those two cases is perfect.

**CASE 1** to make prior integrate to 1, it seems that I have to ignore the possibility of theta falling in the region (1.355,1.356).

So when I estimate determinate model, I set prior region to be [1,1.355];

`theta, , , ,beta_pdf, 1.3, 0.1, 1, 1.355;`

When I estimate indeterminate model, I set prior region to be [1.356, infinity);

`theta, , , ,gamma_pdf, 1.5, 0.1, 1.356, ;`

Then compare the log data density to see which model is more supported…

If CASE 1 is NOT OK,

how about CASE2, where priors together cover [1,infinity), however at the expense of broking “prior integrate to 1” rule-------prior of determinacy model covers a [b] tiny region of indeterminacy, tinier than (1.355,1.356).

**CASE 2**

So when I estimate determinate model, I set prior region to be [1,1.356], ;

`theta, , , ,beta_pdf, 1.3, 0.1, 1, 1.356;`

When I estimate indeterminate model, I set prior region to be [1.356, infinity);

`theta, , , ,gamma_pdf, 1.5, 0.1, 1.356, ;`

Could you tell me if Case1 or Case2 is fine or both wrong?

Best regards,

Huan