by What does concern me about my discipline, however, is that its current core — by which I mainly mean the so-called dynamic stochastic general equilibrium approach — has become so mesmerized with its own internal logic that it has begun to confuse the precision it has achieved about its own world with the precision that it has about the real one … The dynamic stochastic general equilibrium strategy is so attractive, and even plain addictive, because it allows one to generate impulse responses that can be fully described in terms of seemingly scientific statements. The model is an irresistible snake-charmer … So we are left with the tension between a type of answer to which we aspire but that has limited connection with reality (the core) and more sensible but incomplete
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What does concern me about my discipline, however, is that its current core — by which I mainly mean the so-called dynamic stochastic general equilibrium approach — has become so mesmerized with its own internal logic that it has begun to confuse the precision it has achieved about its own world with the precision that it has about the real one …
The dynamic stochastic general equilibrium strategy is so attractive, and even plain addictive, because it allows one to generate impulse responses that can be fully described in terms of seemingly scientific statements. The model is an irresistible snake-charmer … So we are left with the tension between a type of answer to which we aspire but that has limited connection with reality (the core) and more sensible but incomplete answers (the periphery) …
My point is that by some strange herding process the core of macroeconomics seems to transform things that may have been useful modeling short-cuts into a part of a new and artificial “reality,” and now suddenly everyone uses the same language, which in the next iteration gets confused with, and eventually replaces, reality.
Caballero’s article emphasizes that the future is inherently uncertain, particularly when it comes to forecasting and implementing economic policies. No amount of statistical analysis, econometrics, decision theory, or game theory can fully overcome this fundamental reality.
According to Keynes, we live in a world permeated by unmeasurable uncertainty — not quantifiable stochastic risk — which often forces us to make decisions based on anything but “rational expectations.” Keynes rather thinks that we base our expectations on the confidence or “weight” we put on different events and alternatives. To Keynes, expectations are a question of weighing probabilities by “degrees of belief,” beliefs that often have precious little to do with the kind of stochastic probabilistic calculations made by the rational agents as modelled in dynamic stochastic general equilibrium (DSGE) models.
How strange that mainstream economists, as a rule, do not even touch upon these aspects of scientific methodology that seem to be so fundamental and important for anyone trying to understand how we learn and orient ourselves in an uncertain world. An educated guess on why this is a fact would be that Keynes’ concepts are not possible to squeeze into a single calculable numerical “probability.” In the quest for measurable quantities, one turns a blind eye to qualities and looks the other way.
Some time ago yours truly was interviewed by a public radio journalist working on a series on Great Economic Thinkers. We were discussing the monumental failures of the predictions-and-forecasts-business. But — the journalist asked — if these cocksure economists with their ‘rigorous’ and ‘precise mathematical-statistical-econometric models are so wrong again and again — why do they persist in wasting time on it? A just question.