A Monte Carlo procedure for checking identification in DSGE models

We propose a numerical method, based on indirect inference, for checking the identification of a DSGE model. Monte Carlo samples are generated from the model's true structural parameters and a VAR approximation to the reduced form estimated for each sample. We then search for a different set of structural parameters that could potentially also generate these VAR parameters. If we can find such a set, the model is not identified. The test is both an alternative to using the rank condition and also can establish whether there is empirically weak identification.