I am analyzing small open economies with DSGE using Bayesian estimation technique.

As you know, we impose prior distribution about model parameters and in particular, we used to implement

pre-sample analysis when giving priors for exogenous AR (1) parameters.

So, I estimated AR(1) and got estimate of persistence parameter. (0.86)

In addition, I generated residual series and then compute standard deviation of it which is my prior mean of shock. (5.90)

However, we also have to impose prior standard deviation of persistence parameter and std of shock.

My question is that

**Is there any criteria to impose prior standard deviation of them?

**

Actually, I got AR(1) coefficient of 0.86 and arbitrarily imposed fairly loose prior std of 0.4 (half).

` rhoy_star, 0.80, 1E-10, 1, BETA_PDF,0.86,0.40;`

Similarly as for std of shock, I imposed

```
stderr epsy_star, 5.90, 1E-5, , INV_GAMMA_PDF,5.90,2.80;
```

But dynare shows

**Error using set_prior (line 172)
Error in prior for rhoy_star: in a beta distribution with mean 0.860000, the standard error can’t be
larger than 0.346987.**

Bayesian estimation is quite sensitive to the choice of prior, so that I hope to know ‘general’ or ‘good’ way on this.

Thank you.