I wonder how to implement gamma shocks (or other non-normal shocks) in dynare? I guess for these non-normal shocks one would need to do the non-linear transformation to tranform normal shocks. And if the curvature of this non-linear transformation is high, the accuracy of dynare (perturbation) is low.
I wonder if there is a general approach to address this issue. One thing I notice is the perturbation AIM method developed here
Did anyone use it before? In the abstract it says it replies on non-stochastic steady state, so I guess it won’t fit for calculating equity premium with time-varying volatility, as in asset pricing literature with epstein-zin preference?
Thank you for your reply. In that reference, you said “A limitation of Dynare currently is that it does not allow for skewed distributions, i.e. even at order 3 the skewness of the shock distribution is assumed to be 0”
Is it still the case? The skewness is what matters in my model, is there any way to capture the effect of skewness in dynare?
Also, according to 1, perturbation AIM would not solve this problem either, given that its underlying is standard dynare?
perhaps put my question differently, suppose I do the non-linear transformation to normal distribution , such that the resulting distribution has some skewness, and perhaps kurtosis as well. Dynare would be able to capture those higher order moments, right?
Then, I think the problem is the curvature associated with this transformation. That’s why I wonder if perturbation AIM would help