Filtering nonsense economics

13 Apr, 2021 at 09:30 | Posted in Economics | 4 Comments

Following the greatest economic depression since the 1930s, Robert Solow in 2010 gave a prepared statement on “Building a Science of Economics for the Real World” for a hearing in the U. S. Congress. According to Solow modern macroeconomics has not only failed at solving present economic and financial problems, but is “bound” to fail. Building microfounded macromodels on “assuming the economy populated by a representative agent” — consisting of “one single combination worker-owner-consumer-everything-else who plans ahead carefully and lives forever” — do not pass the smell test: does this really make sense? Solow surmised that a thoughtful person “faced with the thought that economic policy was being pursued on this basis, might reasonably wonder what planet he or she is on.”

Conclusion: an economic theory or model that doesn’t pass the real world smell-test is just silly nonsense that doesn’t deserve our attention and therefore belongs in the dustbin.

Microfounded macroeconomic DSGE models immediately come to mind.

Those who want to build macroeconomics on microfoundations usually maintain that the only robust policies and institutions are those based on rational expectations and representative actors. As I tried to show in my paper Rational expectations — a fallacious foundation for macroeconomics in a non-ergodic world — there is really no support for that conviction at all. On the contrary. If we want to have anything of interest to say on real economies, financial crisis and the decisions and choices real people make, it is high time to replace macroeconomic models building on representative actors and rational expectations-microfoundations with more realist and relevant macroeconomic thinking.

If substantive questions about the real world are being posed, it is the formalistic-mathematical representations utilized to analyze them that have to match reality, not the other way around.

Whereas some theoretical models can be immensely useful in developing intuitions, in essence a theoretical model is nothing more than an argument that a set of conclusions follows from a given set of assumptions. Being logically correct may earn a place for a theoretical model on the bookshelf, but when a theoretical model is taken off the shelf and applied to the real world, it is important to question whether the model’s assumptions are in accord with what we know about the world. Is the story behind the model one that captures what is important or is it a fiction that has little connection to what we see in practice? Have important factors been omitted? Are economic agents assumed to be doing things that we have serious doubts they are able to do? These questions and others like them allow us to filter out models that are ill suited to give us genuine insights. To be taken seriously models should pass through the real world filter.

Paul Pfleiderer

Pfleiderer’s perspective may be applied to many of the issues involved when modelling complex and dynamic economic phenomena. Let me take just one example — simplicity.

When it comes to modelling I do see the point often emphatically made for simplicity among economists and econometricians — but only as long as it doesn’t impinge on our truth-seeking. “Simple” macroeconom(etr)ic models may of course be an informative heuristic tool for research. But if practitioners of modern macroeconom(etr)ics do not investigate and make an effort of providing a justification for the credibility of the simplicity-assumptions on which they erect their building, it will not fullfil its tasks. Maintaining that economics is a science in the “true knowledge” business, I remain a skeptic of the pretences and aspirations of  “simple” macroeconom(etr)ic models and theories. So far, I can’t really see that e. g. “simple” microfounded models have yielded very much in terms of realistic and relevant economic knowledge.

All empirical sciences use simplifying or unrealistic assumptions in their modelling activities. That is not the issue – as long as the assumptions made are not unrealistic in the wrong way or for the wrong reasons.

But models do not only face theory. They also have to look to the world. Being able to model a “credible world,” a world that somehow could be considered real or similar to the real world, is not the same as investigating the real world. Even though — as Pfleiderer acknowledges — all theories are false, since they simplify, they may still possibly serve our pursuit of truth. But then they cannot be unrealistic or false in any way. The falsehood or unrealisticness has to be qualified.

Explanation, understanding and prediction of real world phenomena, relations and mechanisms therefore cannot be grounded on simpliciter assuming simplicity. If we cannot show that the mechanisms or causes we isolate and handle in our models are stable, in the sense that what when we export them from are models to our target systems they do not change from one situation to another, then they – considered “simple” or not – only hold under ceteris paribus conditions and a fortiori are of limited value for our understanding, explanation and prediction of our real world target system.

The obvious ontological shortcoming of a basically epistemic – rather than ontological – approach, is that “similarity” or “resemblance” tout court do not guarantee that the correspondence between model and target is interesting, relevant, revealing or somehow adequate in terms of mechanisms, causal powers, capacities or tendencies. No matter how many convoluted refinements of concepts made in the model, if the simplifications made do not result in models similar to reality in the appropriate respects (such as structure, isomorphism etc), the surrogate system becomes a substitute system that does not bridge to the world but rather misses its target.

Constructing simple macroeconomic models somehow seen as “successively approximating” macroeconomic reality, is a rather unimpressive attempt at legitimising using fictitious idealisations for reasons more to do with model tractability than with a genuine interest of understanding and explaining features of real economies. Many of the model assumptions standardly made by neoclassical macroeconomics – simplicity being one of them – are restrictive rather than harmless and could a fortiori anyway not in any sensible meaning be considered approximations at all.

If economists aren’t able to show that the mechanisms or causes that they isolate and handle in their “simple” models are stable in the sense that they do not change when exported to their “target systems”, they do only hold under ceteris paribus conditions and are a fortiori of limited value to our understanding, explanations or predictions of real economic systems.

That Newton’s theory in most regards is simpler than Einstein’s is of no avail. Today Einstein has replaced Newton. The ultimate arbiter of the scientific value of models cannot be simplicity.

As scientists we have to get our priorities right. Ontological under-labouring has to precede epistemology.