Jetro Anttonen

Bayesian inference on fully and partially identified potentially non-Gaussian structural vector autoregressions

We introduce a new approach to Bayesian inference in potentially non-Gaussian structural vector autoregressions. It relies on the result that the elements of the impact matrix are at least set-identified with narrow bounds under standard assumptions. As a result, an efficient simulation algorithm should be capable of e...