by From Lars Syll What does concern me about my discipline … 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 … While it often makes sense to assume rational expectations for a limited application to isolate a particular mechanism that is distinct from the role of expectations formation, this assumption no longer makes sense once we assemble the whole model. Agents could be fully rational with respect to their local environments and everyday activities, but they are most probably nearly clueless with respect to the statistics about which current
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from Lars Syll
What does concern me about my discipline … 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 …
While it often makes sense to assume rational expectations for a limited application to isolate a particular mechanism that is distinct from the role of expectations formation, this assumption no longer makes sense once we assemble the whole model. Agents could be fully rational with respect to their local environments and everyday activities, but they are most probably nearly clueless with respect to the statistics about which current macroeconomic models expect them to have full information and rational information.
This issue is not one that can be addressed by adding a parameter capturing a little bit more risk aversion about macro-economic, rather than local, phenomena. The reaction of human beings to the truly unknown is fundamentally different from the way they deal with the risks associated with a known situation and environment … In realistic, real-time settings, both economic agents and researchers have a very limited understanding of the mechanisms at work. This is an order-of-magnitude less knowledge than our core macroeconomic models currently assume, and hence it is highly likely that the optimal approximation paradigm is quite different from current workhorses, both for academic and policy work. In trying to add a degree of complexity to the current core models, by bringing in aspects of the periphery, we are simultaneously making the rationality assumptions behind that core approach lessplausible …
The challenges are big, but macroeconomists can no longer continue playing internal games. The alternative of leaving all the important stuff to the “policy”type and informal commentators cannot be the right approach. I do not have the answer. But I suspect that whatever the solution ultimately is, we will accelerate our convergence to it, and reduce the damage we do along the transition, if we focus on reducing the extent of our pretense-of-knowledge syndrome.
Caballero’s article underlines — especially when it comes to forecasting and implementing economic policies — that the future in many areas is inherently unknowable, and using statistics, econometrics, decision theory or game theory, does not in the least overcome this ontological fact.
It also further underlines how important it is in social sciences — and economics in particular — to incorporate Keynes’s far-reaching and incisive analysis of induction and evidential weight in his seminal A Treatise on Probability (1921).
How strange that social scientists and mainstream economists, as a rule, do not even touch upon these aspects of scientific methodology that seems 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 puts a blind eye to qualities and looks the other way.
So why do economists, companies and governments continue with the expensive, but obviously worthless, activity of trying to forecast/predict the future?
A couple of months 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 wasting time on it?” Yes, indeed, why do they?