Taylor series (student stuff)

17 May, 2017 at 14:26 | Posted in Economics | Leave a comment


Mainstream economics — an emperor turned out to be naked

15 May, 2017 at 23:41 | Posted in Economics | 3 Comments

The main reason why the teaching of microeconomics (or of “ micro foundations” of macroeconomics) has been called “autistic” is because it is increasingly impossible to discuss real-world economic questions with microeconomists – and with almost all neoclassical theorists. They are trapped in their system, and don’t in fact care about the outside world any more. If you consult any microeconomic textbook, it is full of maths (e.g. Kreps or Mas-Colell, Whinston and Green) or of “tales” (e.g. Varian or Schotter), without real data (occasionally you find “examples”, or “applications”, with numerical examples – but they are purely fictitious, invented by the authors).

an-inconvenient-truth1At first, French students got quite a lot of support from teachers and professors: hundreds of teachers signed petitions backing their movement – specially pleading for “pluralism” in teaching the different ways of approaching economics. But when the students proposed a precise program of studies … almost all teachers refused, considering that is was “too much” because “students must learn all these things, even with some mathematical details”. When you ask them “why?”, the answer usually goes something like this: “Well, even if we, personally, never use the kind of ‘theory’ or ‘tools’ taught in micoreconomics Courses … surely there are people who do ‘use’ and ‘apply’ them, even if it is in an ‘unrealistic’, or ‘excessive’ way”.

But when you ask those scholars who do “use these tools”, especially those who do a lot of econometrics with “representative agent” models, they answer (if you insist quite a bit): “OK, I agree with you that it is nonsense to represent the whole economy by the (intertemporal) choice of one agent –- consumer and producer — or by a unique household that owns a unique firm; but if you don’t do that, you don’t do anything !”

Bernard Guerrien

Yes indeed — “you don’t do anything!”

Twenty years ago Phil Mirowski was invited to give a speech on themes from his book More Heat than Light at my economics department in Lund, Sweden. All the mainstream neoclassical professors were there. Their theories were totally mangled and no one — absolutely no one — had anything to say even remotely reminiscent of a defense. Being at a nonplus, one of them, in total desperation, finally asked “But what shall we do then?”

Yes indeed — what shall they do? The emperor turned out to be naked.

How to live your life (personal)

15 May, 2017 at 17:20 | Posted in Varia | 1 Comment


Among documentaries this is my absolute favourite.
Whenever my soul gets tired, watching this wonderful video of simple — good — life gives me new energy and hope.

You’re done Tommy boy!

15 May, 2017 at 12:12 | Posted in Economics | 1 Comment

I really love this guy. He immediately goes for the essentials. He has no time for bullshit — and neither should we!

Formal mathematical modeling in economics — a dead-end

14 May, 2017 at 21:07 | Posted in Economics | Leave a comment

Using formal mathematical modeling, mainstream economists sure can guarantee that the conclusions hold given the assumptions. However, the validity we get in abstract model worlds does not warrantly transfer to real world economies. Validity may be good, but it isn’t enough. From a realist perspective both relevance and soundness are sine qua non.

broken-linkIn their search for validity, rigour and precision, mainstream macro modellers of various ilks construct microfounded DSGE models that standardly assume rational expectations, Walrasian market clearing, unique equilibria, time invariance, linear separability and homogeneity of both inputs/outputs and technology, infinitely lived intertemporally optimizing representative household/ consumer/producer agents with homothetic and identical preferences, etc., etc. At the same time the models standardly ignore complexity, diversity, uncertainty, coordination problems, non-market clearing prices, real aggregation problems, emergence, expectations formation, etc., etc.

Behavioural and experimental economics — not to speak of psychology — show beyond any doubts that “deep parameters” — peoples’ preferences, choices and forecasts — are regularly influenced by those of other participants in the economy. And how about the homogeneity assumption? And if all actors are the same – why and with whom do they transact? And why does economics have to be exclusively teleological (concerned with intentional states of individuals)? Where are the arguments for that ontological reductionism? And what about collective intentionality and constitutive background rules?

These are all justified questions – so, in what way can one maintain that these models give workable microfoundations for macroeconomics? Science philosopher Nancy Cartwright gives a good hint at how to answer that question:

Our assessment of the probability of effectiveness is only as secure as the weakest link in our reasoning to arrive at that probability. We may have to ignore some issues to make heroic assumptions about them. But that should dramatically weaken our degree of confidence in our final assessment. Rigor isn’t contagious from link to link. If you want a relatively secure conclusion coming out, you’d better be careful that each premise is secure going on.

On a deep level one could argue that the one-eyed focus on validity makes mainstream economics irrelevant, since its insistence on deductive-axiomatic foundations doesn’t earnestly consider the fact that its formal logical reasoning, inferences and arguments show an amazingly weak relationship to their everyday real world equivalents. Although the formal logic focus may deepen our insights into the notion of validity, the rigour and precision has a devastatingly important trade-off: the higher the level of rigour and precision, the smaller is the range of real world application. So the more mainstream economists insist on formal logic validity, the less they have to say about the real world.

Structural econometrics

14 May, 2017 at 18:43 | Posted in Statistics & Econometrics | 2 Comments

In a blog post the other day, Noah Smith returned again to the discussion about the ’empirical revolution’ in economics and how to — if it really does exist — evaluate it. Counter those who think quasi-experiments and RCTs are the true solutions to finding causal parameters, Noah argues that without structural models

empirical results are only locally valid. And you don’t really know how local “local” is. If you find that raising the minimum wage from $10 to $12 doesn’t reduce employment much in Seattle, what does that really tell you about what would happen if you raised it from $10 to $15 in Baltimore?

That’s a good reason to want a good structural model. With a good structural model, you can predict the effects of policies far away from the current state of the world.

If only that were true! But it’s not.

Structural econometrics — essentially going back to the Cowles programme — more or less takes for granted the possibility of a priori postulating relations that describe economic behaviours as invariant within a Walrasian general equilibrium system. In practice that means the structural model is based on a straightjacket delivered by economic theory. Causal inferences in those models are — by assumption — made possible since the econometrician is supposed to know the true structure of the economy. And, of course, those exact assumptions are the crux of the matter. If the assumptions don’t hold, there is no reason whatsoever  to have any faith in the conclusions drawn, since they do not follow from the statistical machinery used!

Structural econometrics aims to infer causes from probabilities, inferred from sample data generated in non-experimental settings. Arguably, it is the most ambitious part of econometrics. It aims to identify economic structures, robust parts of the economy to which interventions can be made to bring about desirable events. This part of econometrics is distinguished from forecasting econometrics in its attempt to capture something of the ‘real’ economy in the hope of allowing policy makers to act on and control events …

LierBy making many strong background assumptions, the deductivist [the conventional logic of structural econometrics] reading of the regression model allows one — in principle — to support a structural reading of the equations and to support many rich causal claims as a result. Here, however, the difficulty is that of finding good evidence for many of the assumptions on which the approach rests. It seems difficult to believe, even in cases where we have good background economic knowledge, that the background information will be sufficiently to do the job that the deductivist asks of it. As a result, the deductivist approach may be difficult to sustain, at least in economics.

The difficulties in providing an evidence base for the deductive approach show just how difficult it is to warrant such strong causal claims. In short, as might be expected there is a trade-off between the strength of causal claims we would like to make from non-experimental data and the possibility of grounding these in evidence. If this conclusion is correct — and an appropriate elaboration were done to take into account the greater sophistication of actual structural econometric methods — then it suggests that if we want to do evidence-based structural econometrics, then we may need to be more modest in the causal knowledge we aim for. Or failing this, we should not act as if our causal claims — those that result from structural econometrics — are fully warranted by the evidence and we should acknowledge that they rest on contingent, conditional assumptions about the economy and the nature of causality.

Damien Fennell

Maintaining that economics is a science in the ‘true knowledge’ business, yours truly remains a skeptic of the pretences and aspirations of — both structural and non-structural — econometrics. So far, I cannot see that it has yielded much in terms of relevant, interesting economic knowledge. Over all the results have been bleak indeed.

Firmly stuck in an empiricist tradition, econometrics is only concerned with the measurable aspects of reality. But there is always the possibility that there are other variables — of vital importance and although perhaps unobservable and non-additive, not necessarily epistemologically inaccessible — that were not considered for the econometric modeling.

Most econometricians still concentrate on fixed parameter models and the structuralist belief/hope that parameter-values estimated in specific spatio-temporal contexts are exportable to totally different contexts. To warrant this assumption one, however, has to convincingly establish that the targeted acting causes are stable and invariant so that they maintain their parametric status after the bridging. The endemic lack of predictive success of the econometric project indicates that this hope of finding fixed parameters is a hope for which there really is no other ground than hope itself.

Most of the assumptions that econometric modeling presupposes  are not only unrealistic — they are plainly wrong.

If economic regularities obtain they do it (as a rule) only because we engineered them for that purpose. Outside man-made ‘nomological machines’ they are rare, or even non-existant. Unfortunately that also makes most of the achievements of both structural and non-structural econometric forecasting and ‘causal explanation’ rather useless.

41svIj0RdVLInvariance assumptions need to be made in order to draw causal conclusions from non-experimental data: parameters are invariant to interventions, and so are errors or their distributions. Exogeneity is another concern. In a real example, as opposed to a hypothetical, real questions would have to be asked about these assumptions. Why are the equations “structural,” in the sense that the required invariance assumptions hold true? Applied papers seldom address such assumptions, or the narrower statistical assumptions: for instance, why are errors IID?

The tension here is worth considering. We want to use regression to draw causal inferences from non-experimental data. To do that, we need to know that certain parameters and certain distributions would remain invariant if we were to intervene. Invariance can seldom be demonstrated experimentally. If it could, we probably wouldn’t be discussing invariance assumptions. What then is the source of the knowledge?

“Economic theory” seems like a natural answer, but an incomplete one. Theory has to be anchored in reality. Sooner or later, invariance needs empirical demonstration, which is easier said than done.

Truth and economics (II)

13 May, 2017 at 12:00 | Posted in Economics | Leave a comment

Reading some of the comments on my earlier post on the status of truth in ‘modern’ economics, yours truly came to think of Robert Solow’s assessment of ludicrously ‘post-real’ model assumptions …

4703325Suppose someone sits down where you are sitting right now and announces to me that he is Napoleon Bonaparte. The last thing I want to do with him is to get involved in a technical discussion of cavalry tactics at the battle of Austerlitz. If I do that, I’m getting tacitly drawn into the game that he is Napoleon. Now, Bob Lucas and Tom Sargent like nothing better than to get drawn into technical discussions, because then you have tacitly gone along with their fundamental assumptions; your attention is attracted away from the basic weakness of the whole story. Since I find that fundamental framework ludicrous, I respond by treating it as ludicrous – that is, by laughing at it – so as not to fall into the trap of taking it seriously and passing on to matters of technique.

Robert Solow

So much for the ’empirical’ revolution in economics

13 May, 2017 at 10:43 | Posted in Economics | 1 Comment

Sometimes a picture is worth a thousand words …

Source: Merijn Knibbe

Thought of kissing my wife in public today, but on second thought …

13 May, 2017 at 10:27 | Posted in Varia | 2 Comments


Blah blah blah economics

12 May, 2017 at 23:05 | Posted in Economics | 1 Comment

A key part of the solution to the identification problem that Lucas and Sargent (1979) seemed to offer was that mathematical deduction could pin down some parameters in a simultaneous system. But solving the identification problem means feeding facts with truth values that can be assessed, yet math cannot establish the truth value of a fact. Never has. Never will.

blah_blahIn practice, what math does is let macroeconomists locate the FWUTVs [facts with unknown truth values] farther away from the discussion of identification … Relying on a micro-foundation lets an author say, “Assume A, assume B, …  blah blah blah …. And so we have proven that P is true. Then the model is identified.” …

Distributional assumptions about error terms are a good place to bury things because hardly anyone pays attention to them. Moreover, if a critic does see that this is the identifying assumption, how can she win an argument about the true expected value the level of aether? If the author can make up an imaginary variable, “because I say so” seems like a pretty convincing answer to any question about its properties.

Paul Romer

« Previous PageNext Page »

Create a free website or blog at WordPress.com.
Entries and comments feeds.