"To an outsider or newcomer, macroeconomics would seem like a field that is haunted by its lack of data, especially good clean experimental data. In the absence of that data, it would seem like we would be hard put to distinguish among a host of theories with distinct policy recommendations. So, to the novice, it would seem like macroeconomists should be plagued by underidentification or partial identification. But, in fact, expert macroeconomists know that the field is actually plagued by failures to fit the data – that is, by overidentification. Why is the novice so wrong? The answer is the role of a priori restrictions in macroeconomic theory. Macroeconomists use a body of theory that imposes a number of a priori parametric restrictions on households and businesses. Households maximize expected discounted utility flows, with utility functions that are required to lie in a narrowly specified class. Businesses maximize profits and engage in monopolistic competition (or perfect competition). Everyone updates their beliefs according to a priori specified rules of some kind (usually Bayesian updating). The mistake that the novice made is to think that the macroeconomist would rely on data alone to build up his/her theory or model. The expert knows how to build up theory from a priori restrictions that are accepted by a large number of scholars [..] this approach to macro made a lot of sense in the mid-1970s or early 1980s. Data and estimation were both much more expensive than they are today. It was (I think) reasonable to substitute relatively cheap theory-driven restrictions for relatively expensive data-driven information."