Dangers of ‘running with the mainstream pack’

18 September, 2017 at 14:43 | Posted in Economics | 3 Comments


An absolutely fabulous speech — and Soskice and Carlin’s textbook Macroeconomics: Institutions, Instability, and the Financial System — that Dullien mentions at the beginning of his speech — is really a very good example of the problems you run into if you want to be ‘pluralist’ within the mainstream pack.

wendyCarlin and Soskice explicitly adapts a ‘New Keynesian’ framework including price rigidities and adding a financial system to the usual neoclassical macroeconomic set-up. But although I find things like the latter amendment an improvement, it’s definitely more difficult to swallow their methodological stance, and especially their non-problematized acceptance of the need for macroeconomic microfoundations.

Some months ago, another sorta-kinda ‘New Keynesian’, Paul Krugman, argued on his blog that the problem with the academic profession is that some macroeconomists aren’t “bothered to actually figure out” how the New Keynesian model with its Euler conditions — “based on the assumption that people have perfect access to capital markets, so that they can borrow and lend at the same rate” — really works. According to Krugman, this shouldn’t be hard at all — “at least it shouldn’t be for anyone with a graduate training in economics.”

Carlin & Soskice seem to share Krugman’s attitude. From the first page of the book, they start to elaborate their preferred 3-equations ‘New Keynesian’ macromodel. And after twenty-two pages, they have already come to specifying the demand side with the help of the Permanent Income Hypothesis and its Euler equations.

But if people — not the representative agent — at least sometimes can’t help being off their labour supply curve — as in the real world — then what are these hordes of Euler equations that you find ad nauseam in these ‘New Keynesian’ macromodels gonna help us? Yours truly’s doubts regarding the ‘New Keynesian’ modellers ‘ obsession with Euler equations is basically that, as with so many other assumptions in ‘modern’ macroeconomics, the Euler equations don’t fit reality.

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.

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 our models to our target systems they do not change from one situation to another, then they 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.

No matter how many convoluted refinements of concepts made in the model, if the “successive approximations” 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.

From this methodological perspective, yours truly has to conclude that Carlin’s and Soskice’s microfounded macroeconomic model is a rather unimpressive attempt at legitimizing using fictitious idealizations — such as Euler equations — for reasons more to do with model tractability than with a genuine interest in understanding and explaining features of real economies.

Running with the mainstream pack is not a good idea if you want to develop realist and relevant economics.

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3 Comments

  1. “. . . as long as the assumptions made are not unrealistic in the wrong way or for the wrong reasons.”
    .
    Probably, you have made adjustments weeks or months ago to your accustomed English phrasing, but I thought this blog post much improved in conveying your argument, compared to a year ago.
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    It remains a difficult point to express, but at least you are not tripping on English idiom.
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    Fundamentally, I think the problem remains, uncertainty. “model tractability” is an issue, because a pervasive uncertainty obviates many of the clever analytic bits of “rational behavior” economists value so much. It is not possible to posit an axiomatic uncertainty, insofar as I can see, or if it is possible, having assumed uncertainty, it is then no longer possible to make rationalist behavioral mechanisms produce definite choices. You certainly cannot assume profit maximizing under uncertainty: it makes no sense to “maximize” under conditions that make calculation itself of only limited effectiveness. People in the real world are not generally paralyzed by uncertainty — they find ways to proceed, but it makes no sense to call their behavior, “maximizing”.
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    In many respects, uncertainty turns the Chicago market economy model upside down and inside out. In the market model, marginal cost is rising, market-clearing equilibria are possible, the system is, as you would say, ergodic. In real life, marginal cost is falling, price must be administered, and, since the actors are learning, the system is path-dependent. Look around, and one sees an economy largely organized around economic rents by bureaucracies, not competitive markets, coordinated by money finance which is anything but a neutral numeraire.
    .
    The most remarkable thing about mainstream macroeconomics is how wholly self-deceived are its practitioners.

    • “People in the real world are not generally paralyzed by uncertainty — they find ways to proceed, but it makes no sense to call their behavior, “maximizing”.”

      Say we assume profit-maximization as a motive for a hedge fund. A hedge fund can use math to eliminate downside uncertainty. Represent all possible market states as prices in a vector; rows represent one future state with each number representing a different stock price; columns represent one stock across all possible states. If I were programming this I would use 0, 1, -1 to represent the stock’s price movements. Then if you have 4 stocks, if I remember permutations right it is 4^3 or 64 rows in a matrix representing all possible future states of the prices of 4 stocks. Then you put your portfolio in a vector x. The portfolio vector consists of a column with each number representing the number of shares you buy from the 4-stock toy market. Then you put your minimum desired payoff in a vector b. Sum the output of A times x to get your net position on the portfolio x. Then you run different portfolios through the equation to get the best ones that deliver an output that is greater than or equal to your minimum desired output. The only uncertainty is how much you will profit.

      The only uncertainty is that something outside the market, some psychological or political force, will interfere somehow and prevent me from getting the guaranteed profit. Maybe someone hacks my bank account, or deletes NASDAQ, or something.

      If there’s a bug in my program, I have to count on funding liquidity to continue. If I have friends with balance sheets large enough to support another loan to me without affecting their lifestyle in the slightest, I can make some mistakes and correct my program …

      So the uncertainties in my model are psychological, political. We should craft public policy that eliminates uncertainty about everyone’s access to vast, persistent surpluses …

      If I write my linear algebra optimization perfect hedging program and release it free and open source, what would that do to markets?

      • Some years ago, I vaguely remember that someone took a quiz to a meeting of the American Economics Association, to see how many economists could correctly apply the concept of “opportunity cost” to some stylized choice between buying a concert ticket or going to a less desirable, but free musical performance. Of course, even that stylized question was confusing — “opportunity cost” as a concept seems designed to draw the mind into confusion — and lots of economists could not work out the “correct” answer.
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        But, I wonder how many economists could, off the cuff, explain what the eponymous “hedge” is. What is that category of bet that gives hedge funds their name? I wonder how many devotees of representative agent models could explain even such simple concepts as arbitrage or short-selling. How many governors of a central bank with an economics background could explain the esoterica of rehypothecation?
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        You posit the use of “profit-maximization” as a label for the motive of greed driving rationalization and calculation, which is a common enough use of the term. Fair enough.
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        In the idealized analysis of theory, where uncertainty is banned by convenient assumption, the profit-maximizer is not an unscrupulous schemer; she is, instead, imagined as a prudent visionary, endowed with rational expectations and a “true” understanding of the world shared universally. In an older framework, we might say that she operates with complete and perfect information.
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        Uncertainty is a tricky thing to fully appreciate. It is not, as Samuel Goldwyn had it, that nobody knows nuthin’ in this business. Nor is uncertainty limited to the occasional black swan swimming unexpectedly into view. It is, rather, that some people know some things, but are not quite sure of the limits of their knowledge — or the knowledge of others. The complex web of financial institutions enables cooperation among the differently and imperfectly informed. The so-called informational efficiency of financial markets is the effective disclosure of all knowledge to all players. When those institutions work very, very well, we get in the real world, something approaching “rational expectations” and those “rational expectations” show up as people treating financial market prices and ratings ‘as if’ they were objective facts, superior to any individual or personal subjective assessment. Relying on market prices and inferences from market prices becomes a dominant strategy, dominating individual subjective assessment. But, of course, in the real world, if you have one large group of investors relying mechanically on such an approach to the limits of perfection represented by “rational expectations” that the market has so efficiently melded all information and knowledge, structuring portfolios accordingly (say, by relying on AAa bond ratings as accurate), some others may find ways to profit by widening the gap of informational efficiency at the margin.
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        So, you ask, what would be the effect of releasing your toy program into the wild? If it using it made someone money initially, some one else would notice and find ways to cheat the first person. That’s the reality of financial markets: not that every opportunity to pick up $20 or pennies off the street has been fully exploited, but every opportunity to con some sucker by faking the discovery of opportunities is being tried. This is the reality that is being ignored and obscured by forming a world-view out of the analytic idealism of representative agents, Euler equations and ad hoc New Keynesian “frictions”.
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        Actual financial markets are driven forward not by the supremely prudential calculations of a profit maximizer with the complete and perfect information of rational expectations, but by the scheming of the greedy, barely restrained by the provision of the public goods of restraint, arbitration, and altruistic punishment, aka regulation. Financial markets — and social cooperation in economic production and distribution in general — work well when private self-seeking is balanced in some way by a public spirit.


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