Back to square one: identification issues in DSGE models

We investigate identifiability issues in DSGE models and their consequences for
parameter estimation and model evaluation whenthe objective function measures
the distance between estimated and model impulse responses. We show that
observational equivalence, partial and weak identification problems are widespread,that
they lead to biased estimates, unreliable t-statistics and may induce investigators to
select false models. We examine whether different objective functions affect identification
and study how small samples interact with parameters and shock identification.
We provide diagnostics and tests to detect identification failures and apply them to a
state-of-the-art model.