setting priors following inverse gamma distribution
Posted: Fri Mar 11, 2016 6:13 am
Usually setting priors in estimation using dynare, we specify two parameters, mean and std.dev.
However, as for parameters following inverse gamma distributions (usually magnitude of shocks), the two are not mean and std.dev but two hyperparameters,s and nu.
As far as I know, the function below computes these two hyper parameters.
function [s,nu] = inverse_gamma_specification(mu, sigma2, lb, type, use_fzero_flag, name)
In my case, I implemented pre-sample analysis (AR-1) to have parameters of persistence and magnitude of shock.
The former is nothing but autoregressive coefficient (rho_x) but what about the latter?
Now, I want to compute s and nu by using the above function but do not know how to specify first two arguments, mu and sigma2.
Regarding mu, I could use std.dev of residuals obtained from the AR(1) estimates. But what about variance?
Is there any criterion in setting s and nu for parameters of the magnitude of shock?
However, as for parameters following inverse gamma distributions (usually magnitude of shocks), the two are not mean and std.dev but two hyperparameters,s and nu.
As far as I know, the function below computes these two hyper parameters.
function [s,nu] = inverse_gamma_specification(mu, sigma2, lb, type, use_fzero_flag, name)
In my case, I implemented pre-sample analysis (AR-1) to have parameters of persistence and magnitude of shock.
The former is nothing but autoregressive coefficient (rho_x) but what about the latter?
Now, I want to compute s and nu by using the above function but do not know how to specify first two arguments, mu and sigma2.
Regarding mu, I could use std.dev of residuals obtained from the AR(1) estimates. But what about variance?
Is there any criterion in setting s and nu for parameters of the magnitude of shock?