This is a consequence of the fact that models are, by construction, an abstraction from reality. Striking the right balance between theory and data fit is more an exercise in judgment than an empirical one, and those judgments are best formed by drawing upon complementary models.
Another lesson is that central banks have traditionally stuck with their models until well after their “best before date.” This is no doubt because greater realism in models means greater complexity, more computing power and big investments in research and people.