Disequilibrium economics is a logical impossibility

This is going to be super abstract, potentially infuriating and probably wrong.

I sometimes hear people talk about „disequilibrium economics“ and I think I know what they have in mind. Equilibrium is often associated with a system at rest. That’s the physicist’s notion of equilibrium: a ball sitting at the bottom of a bowl, a planet moving around the sun in a stable orbit, etc. Disequilibrium is something not at rest: you hit the ball and it jiggles around inside the bowl, a planet collides with another and flies off its orbit.

Economists have a different notion of equilibrium. Indeed, they have several different notions depending on the context. But basically, an economic equilibrium is a consistency condition imposed on a model by the economist. It follows that „disequilibrium economics“ is a logical impossibility.

Let me explain. Economists build models to explain certain real-world phenomena, say bank runs. Inside these models there are agents, e.g. savers, banks, firms, each described by their preferences, beliefs and constraints. For instance, a saver wants to keep her money in the bank as long as she believes she will get it back eventually. Whether she can get it back depends on the number of savers who demand their money back. As long as most of them don’t want to withdraw their money, everything is fine. However, if there is a critical mass of savers who want their money back, the bank needs to liquidate its assets prematurely at „fire-sale“ prices, which means it cannot repay all the savers’ deposits in full. You have two equilibria: one in which nobody runs on the banks, the banks carry their investments to maturity, everyone gets repaid; another one in which everyone runs, the banks liquidate their investments prematurely, people don’t get repaid in full.

Only the first of these equilibria can sensibly be characterized as „a system at rest“. In the second equilibrium, nothing is at rest: there is chaos in the streets, banks go bust and people get hurt.

What characterizes both equilibria are two conditions:

  1. Everyone is doing the right thing given their preferences, beliefs, and constraints. The saver who runs on the bank is doing the right thing: Given that everyone else runs, she should run, too, or else she will get nothing. This is called rational behavior, but it should really be called consistent behavior. It’s behavior that is consistent with an agent’s preferences, beliefs and constraints.
  2. Things need to add up. Or to put in fancier language: individual decisions need to be consistent with each other. The total value of deposits repaid cannot exceed the total value of assets held by the banks. If there are 10 cookies and I want to eat 8 and you want to eat 5, that’s not an equilibrium. It’s a „disequilibrium“. It’s a logical impossibility.

If you’re a behavioral economist, you may take issue with condition (1). You may argue that people often don’t do the right thing, they are confused about their beliefs and they don’t understand their constraints very well. That’s fine with me. Let agents do their behavioral thing and make mistakes. (Although you must be explicit about which mistake out of the approximately infinite number of mistakes they could make they actually do make.) But still, things need to add up. I may be mistaken to want 8 cookies and you may be confused to want 5, but there are still only 10 cookies. Behavioral economics still needs condition (2).

If you’re a first-year undergrad, you may think equilibrium means that markets clear. Then you learn about asymmetric information and realize that things like credit rationing can occur in equilibrium. And you learn about the search models. Adding up constraints may be inequality constraints.

Finally, you cannot „test for equilibrium“ with data. Equilibrium is that which your model predicts. If your prediction is contradicted by the data, it’s because your model is wrong, not because there is „disequilibrium“. I have heard econometricians talk about error correction models where they call the error correction term a measure of „disequilibrium“. What they mean by that is that their economic model can only explain the long-run relationship between variables (the cointegration part), from which there are unexplained short-run deviations. But that just means the model is wrong for these short-run movements.

Equilibrium means consistency at the individual and at the aggregate level. It doesn’t mean stable, it doesn’t mean perfect. In fact, it is completely devoid of empirical content in and of itself. It only becomes meaningful in the context of a concrete model. And without it, economic models wouldn’t make any sense.

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Why I like DSGE models

Christoph has recently vented his frustration about “DSGE bashing” now popular in the econ blogosphere. I feel this frustration, too, not because I believe DSGEs are perfect, but because I think that much of the popular criticism is ill-informed. Since I have worked with DSGE models recently in my research, I can call myself a card-carrying member of the club of DSGE aficionados. So I thought I briefly explain why I like DSGEs and what I think they are good for.

I think of DSGE models as applying ordinary principles of economics – optimizing behavior and market equilibrium (GE for general equilibrium) – to a world that evolves over time (D for dynamic) and is subject to chance (S for stochastic). When I say optimizing behavior I don’t necessarily mean rational expectations and when I say equilibrium I don’t necessarily mean market clearing. There are DSGEs without rational expectations and with non-clearing markets out there, although admittedly they are not the most widely used ones. I find this general approach attractive, because it brings us closer to a Unified Economic Science that uses a single set of principles to explain phenomena at the micro level and at the macro level.

But that’s not the most important reason I like DSGEs, which is that it makes precise and thus helps clarify commonly held notions about business cycles, economic crises and economic. Take, for instance, the notion of “recession”. In popular discussion a “recession” is when GDP growth is negative or at least below what is conceived a normal or desirable rate. In DSGE models, a recession is a negative output gap: the difference between the actual level of output and that level which would occur if prices were fully flexible (the “natural rate of output”). DSGEs make it clear that a negative growth rate is not necessarily bad (if the weather is bad in April and better in May, you want production to go down in April and up in May) and a positive growth rate not necessarily good (two percent real growth can sometimes mean an overheating economy and sometimes be a sluggish one). You have to look at more than one variable (at least two, output growth and inflation) to decide whether the economy is in good or bad shape.

Another reason I like DSGEs is that they discuss economic policy in a much more coherent and sensible manner than most of the earlier literature – and much more so than the financial press. The important question about any policy X is not “Does X increase GDP or reduce unemployment or increase asset prices?”, but “Does X increase the utility of households?”. Also, because DSGEs are dynamic models, they put the focus on policy rules, i.e. how policymakers behave across time and in different situations, instead of looking only what policymakers do right now and in this particular situation.

There is a lot of valid criticism against DSGEs: they often are too simplistic and sweep important but hard-to-model aspects under the rug and they, as a result of that, have lots of empirical issues. But these things should encourage us to make DSGEs better, not return to the even more simplistic approaches that previously dominated macroeconomics.

How to judge (macro)economic models or Why Paul Krugman gets it wrong

Paul Krugman recently participated in a discussion about the current state of macroeconomics, particularly about the “dominant” paradigm of DSGE models and their predecessor, the IS-LM model. Using DSGE models by myself and disagreeing with basically all of what he said, let me comment on an especially unconvincing piece of reasoning:

“[…] how [do] we know that a modeling approach is truly useful. The answer, I’d suggest, is that we look for surprising successful predictions. So has there been anything like that in recent years? Yes: economists who knew and still took seriously good old-fashioned Hicksian IS-LM type analysis made some strong predictions after the financial crisis”.

In short: forget DSGE models and related stuff and go back to IS-LM because people using IS-LM recently made some “right” predictions. Is the exclusive focus on its “predictions”a reasonable criterion to judge (macro)economic models or how should they be judged else?

With respect to the former, let me give you an admittedly extreme example. Over the last few decades, we could have very well predicted the EU agricultural policy by assuming that the aim of policy makers was to reduce consumer welfare. Would you resort to this sort of model when discussing likely upcoming agricultural policies from an outsider perspective? I would not.

Can we take up another extreme and simply judge a model based on the realism of its assumptions? I suggest we can’t. “Let’s assume a representative agent who cares about consumption, leisure and wearing blue jeans” to take another extreme example. Would you reject a macroeconomic argument formulated with this agent based on the unrealism of the blue jean assumption (as long as the model is not intended to explain the jeans market). I would not because in this context, I regard this assumption to be irrelevant for the models conclusions/predictions. The difference with respect to the first example is then of course that in the former, I’m convinced that the assumption matters for the results, in the latter I´m convinced it does not.

So one cannot judge models solely by their predictions because the underlying assumptions in combination with the implied propagation mechanisms might be clearly implausible/unconvincing and important for the predictions. Assessing the latter in turn requires to dig into the model dynamics implying that one can also not base a judgement solely on the realism of the assumptions.

How can a reasonable criterion that takes the above findings under consideration then look like? As so beautifully described in McCloskey essay on the “Rhetoric of Economics”, (macro)economists are persuaders. What they do is “careful weighing of more or less good reasons to arrive at more or less probable or plausible conclusions – none too secure, but better than would be arrived by chance or unthinking impulse; it is the art of discovering warrantable beliefs and improving those beliefs in shared discourse […] (Booth 1961, see the above link p. 483 for the exact citation)”. The purpose of models, I’d argue is then to help organizing that discourse, help to structure your own thinking, clarify the debate and provide a framework to confront thought with data.  In order to be useful for that, they need to be “persuasive” in the context they are used. The realism of the assumptions and the implied propagation mechanisms, their respective importance for the results and the model´s fit to data are all part of the subjective assessment with respect to that criterion.

How then about DSGE vs. IS-LM in policy debate? IS-LM and related models were mainly discarded in policy analysis because they incorporate reduced form parameters on e.g. the interest rate responsiveness of investment or the marginal propensity to consume which most economists were not convinced to be sufficiently independent of economic policy. This criticism is today as valid as it was 30 or 40 years ago, none of that has changed. All that has changed is that IS-LM made some “correct” (one may very well discuss the use of this word here but that´s not the purpose of this blog entry) predictions. Shall we go back to a model that was labelled as an unpersuasive tool although the main point of criticism is still valid – NO. Shall we use it now lacking a better alternative? Properly specified state-of-the-art DSGE models are a tool that outclasses IS-LM on virtually every aspect (YES even when it comes to rational expectations). For sake of shortening the entry, I will yet argue my case for that in a follow-up post.

How to make students honest in exams

One of my favorite economists, David Friedman, suggests an economic solution to a problem that every teacher has probably faced.

In most exams, students have an incentive to respond to a question even if they do not know the answer. If they do not respond at all, they will get zero points with certainty. If they write something – anything – there is some probability that they will get at least a few points, maybe because they guessed the correct answer or because the teacher reads what he or she wants to read.

Pretending to know the answer when you don’t is an economically wasteful activity. It is a waste of time for the student as well as for the teacher who has to grade the exam. It is also, at least potentially, a distortion of the signal embodied in exam grades, because students who pretend to know might do as well on the exam as students who really know.

Friedman’s solution: Award 20 percent of the points for the response “I don’t know”. Students who know less than 20 percent or are less than 20 percent sure that they know the right answer will respond, rationally and honestly, “I don’t know”. Students who know more or are more certain of their knowledge will give their answer.

Now behavioral economists might object that students may be overconfident, i.e. they overestimate their true abilities and give an answer not because they pretend to know, but rather because they truly believe they know. However, even overconfident students – those who, for instance, put the probability that they know the right answer at 50 percent when in fact they don’t know it – might still prefer to answer “I don’t know”, because that guarantees them a certain outcome of 20 percent of the points whereas writing an response they are unsure about means risking losing all the points on the question.

Anyway, I love the solution. I think I will try it in my course. It is also a good example of how simple economics – thinking through the implications of rational behavior – helps solving a non-economic problem.

Monthly Proposal No.8: try to match up legality and legitimacy

This time it once more does not seem to be an economic advice. However, in some regard lawyers and economists make the same mistake. When they have to discuss an issue and at best provide some conclusion or judgement, they choose a certain framework as given and claim their implications as positive results. Though, as long as these frameworks are built on definitions and assumptions, it is normative. So, even if their results are derived and documented by complex analytics or finest verbalism and citation, they have to be considered as normative as well.

Of course, that does not imply that these results are automatically wrong. However, it has to be accepted that they can be criticised just by normative arguments, because in the end they are not more than that too. That something is established and considered as law or common sense therefor is not a giving of evidence for its legitimacy. The latter always can and have to be taken in question.

So whether a compulsory acquisition is an illegitimate measure depends whether the property was accumulated legitimately – not just legally. Similar thoughts can be seized about the height of market based income, the sentence on tax evasion and the judgements on political corruption. No one is above the law, but in the end the stated law is not naturally given. It is nothing more than a temporary chosen framework, built on normative definitions and assumptions – just like the mainstream economic theory – and most important: it can be corrected.