On the impossibility of efficient markets

17 Jan, 2020 at 18:46 | Posted in Economics | 6 Comments

In general the price system does not reveal all the information about “the true value” of the risky asset …

tumblr_n6vk0tAVwh1rlnhn7o1_500The only way informed traders can earn a return on their activity of information gathering, is if they can use their information to take positions in the market which are “better” than the positions of uninformed traders. “Efficient Markets” theorists have claimed that “at any time prices fully reflect all available information” … If this were so then informed traders could not earn a return on their information.

When the efficient markets hypothesis is true and information is costly, competitive markets break down … As soon as the assumptions of the conventional perfect capital markets model are modified to allow even a slight amount of information imperfection and a slight cost of information, the traditional theory becomes untenable. There cannot be as many securities as states of nature. For if there were, competitive equilibrium would not exist …

Because information is costly, prices cannot perfectly reflect the information which is available, since if it did, those who spent resources to obtain it would receive no compensation. There is a fundamental conflict between the efficiency with which markets spread information and the incentives to acquire information.

Sanford Grossman & Joseph Stiglitz

Here (in Swedish) is my own take on the paradox.

Friday on my mind

17 Jan, 2020 at 10:11 | Posted in Varia | 1 Comment

 

Dynamic and static interpretations of regression coefficients (wonkish)

15 Jan, 2020 at 19:33 | Posted in Statistics & Econometrics | Leave a comment

When econometric and statistical textbooks present simple (and multiple) regression analysis for cross-sectional data, they often do it with regressions like “regress test score (y) on study hours (x)” and get the result

y = constant + slope coefficient*x + error term.

UnknownWhen speaking of increases or decreases in x in these interpretations, we have to remember that it is a question of cross-sectional data and ‘increases’– which means that we are referring to increases in the value of a variable from one unit in the population to another unit in the same population. Strictly seen it is only admissible to give slope coefficients a dynamic interpretation when we are dealing with time-series regression. For cross-sectional data, we should stick to static interpretations and look upon slope coefficients as giving information about what we can expect to happen to the value of the dependent variable when there is a change in the independent variable from one unit to another.

Although it is tempting to say that a change in the independent variable leads to a change in the dependent variable, we should resist that temptation. Students that put a lot of study hours into their daily routine on average achieve higher scores on their tests than other students that study for fewer hours. But — the regressions made do not analyse what happens to individual students as they increase or decrease their study hours.

Why is this important? It is important most of all because interpreting the regression coefficients wrong may give a totally wrong causal view of what is going on in your data. A positive relation between test scores and study hours in a cross-sectional regression does not mean that you as an individual student should expect to get higher test scores by increasing study time.

Why all RCTs are biased

15 Jan, 2020 at 17:22 | Posted in Statistics & Econometrics | Leave a comment

Randomised experiments require much more than just randomising an experiment to identify a treatment’s effectiveness. They involve many decisions and complex steps that bring their own assumptions and degree of bias before, during and after randomisation …

rcSome researchers may respond, “are RCTs not still more credible than these other methods even if they may have biases?” For most questions we are interested in, RCTs cannot be more credible because they cannot be applied (as outlined above). Other methods (such as observational studies) are needed for many questions not amendable to randomisation but also at times to help design trials, interpret and validate their results, provide further insight on the broader conditions under which treatments may work, among other rea- sons discussed earlier. Different methods are thus complements (not rivals) in improving understanding.

Finally, randomisation does not always even out everything well at the baseline and it cannot control for endline imbalances in background influencers. No researcher should thus just generate a single randomisation schedule and then use it to run an experiment. Instead researchers need to run a set of randomisation iterations before conducting a trial and select the one with the most balanced distribution of background influencers between trial groups, and then also control for changes in those background influencers during the trial by collecting endline data. Though if researchers hold onto the belief that flipping a coin brings us closer to scientific rigour and understanding than for example systematically ensuring participants are distributed well at baseline and endline, then scientific understanding will be undermined in the name of computer-based randomisation.

Alexander Krauss

The point of making a randomized experiment is often said to be that it ‘ensures’ that any correlation between a supposed cause and effect indicates a causal relation. This is believed to hold since randomization (allegedly) ensures that a supposed causal variable does not correlate with other variables that may influence the effect.

The problem with that simplistic view on randomization is that the claims made are both exaggerated and false:

• Even if you manage to do the assignment to treatment and control groups ideally random, the sample selection certainly is — except in extremely rare cases — not random. Even if we make a proper randomized assignment, if we apply the results to a biased sample, there is always the risk that the experimental findings will not apply. What works ‘there,’ does not work ‘here.’ Randomization hence does not ‘guarantee ‘ or ‘ensure’ making the right causal claim. Although randomization may help us rule out certain possible causal claims, randomization per se does not guarantee anything!

• Even if both sampling and assignment are made in an ideal random way, performing standard randomized experiments only give you averages. The problem here is that although we may get an estimate of the ‘true’ average causal effect, this may ‘mask’ important heterogeneous effects of a causal nature. Although we get the right answer of the average causal effect being 0, those who are ‘treated’  may have causal effects equal to -100 and those ‘not treated’ may have causal effects equal to 100. Contemplating being treated or not, most people would probably be interested in knowing about this underlying heterogeneity and would not consider the average effect particularly enlightening.

• There is almost always a trade-off between bias and precision. In real-world settings, a little bias often does not overtrump greater precision. And — most importantly — in case we have a population with sizeable heterogeneity, the average treatment effect of the sample may differ substantially from the average treatment effect in the population. If so, the value of any extrapolating inferences made from trial samples to other populations is highly questionable.

• Since most real-world experiments and trials build on performing a single randomization, what would happen if you kept on randomizing forever, does not help you to ‘ensure’ or ‘guarantee’ that you do not make false causal conclusions in the one particular randomized experiment you actually do perform. It is indeed difficult to see why thinking about what you know you will never do, would make you happy about what you actually do.

Randomization is not a panacea. It is not the best method for all questions and circumstances. Proponents of randomization make claims about its ability to deliver causal knowledge that are simply wrong. There are good reasons to be sceptical of the now popular — and ill-informed — view that randomization is the only valid and best method on the market. It is not.

Chicago economics — only for Gods and Idiots

15 Jan, 2020 at 14:27 | Posted in Economics | 2 Comments

4703325-2If I ask myself what I could legitimately assume a person to have rational expectations about, the technical answer would be, I think, about the realization of a stationary stochastic process, such as the outcome of the toss of a coin or anything that can be modeled as the outcome of a random process that is stationary. I don’t think that the economic implications of the outbreak of World war II were regarded by most people as the realization of a stationary stochastic process. In that case, the concept of rational expectations does not make any sense. Similarly, the major innovations cannot be thought of as the outcome of a random process. In that case the probability calculus does not apply.

Robert Solow

‘Modern’ macroeconomic theories are as a rule founded on the assumption of rational expectations — where the world evolves in accordance with fully predetermined models where uncertainty has been reduced to stochastic risk describable by some probabilistic distribution.

The tiny little problem that there is no hard empirical evidence that verifies these models — cf. Michael Lovell (1986) and Nikolay Gertchev (2007) — usually doesn’t bother its protagonists too much. Rational expectations überpriest Thomas Sargent has the following to say on the epistemological status of the rational expectations hypothesis:

Partly because it focuses on outcomes and does not pretend to have behavioral content, the hypothesis of rational epectations has proved to be a powerful tool for making precise statements about complicated dynamic economic systems.

Precise, yes, in the celestial world of models. But relevant and realistic? I’ll be dipped!

And a few years later, when asked if he thought “that differences among people’s models are important aspects of macroeconomic policy debates”, Sargent replied:

The fact is you simply cannot talk about their differences within the typical rational expectations model. There is a communism of models. All agents within the model, the econometricians, and God share the same model.

Building models on rational expectations either means we are Gods or Idiots. Most of us know we are neither. So, Gods and Idiots may share Sargent’s and Lucas’s models, but they certainly aren’t my models.

Et maintenant

14 Jan, 2020 at 19:16 | Posted in Varia | 1 Comment

 

Economists saving the world …

14 Jan, 2020 at 10:42 | Posted in Varia | 1 Comment

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Lagen om valfrihetssystem (LOV) — en nyliberal våtdröm

13 Jan, 2020 at 13:22 | Posted in Politics & Society | Leave a comment

De välvilliga etableringsreglerna i LOV och andra sammanhang har öppnat vägen för den ekonomiska brottsligheten. Skurkarna har brett ut sig, lycksökarna har lekt lotsar inom arbetsmarknadspolitiken, men varken skolan eller sjukvården har gått fri från svindlare. Och vart tog kvaliteten vägen, den som skulle ursäkta allt det andra?

Skärmavbild-2017-01-12-kl.-08.38.20Grundfelet i LOV-ideologin är att den utgår från att kvaliteten skulle vara lätt att urskilja för de välinformerade konsumenter som väljer mellan utförarna. Varken det ena eller det andra stämmer.

Många av välfärdens tjänster är komplexa, svåra att granska för dem som står utanför professionen, och dessutom långsiktiga – det kan dröja åratal och mer innan det visar sig hur bra skolan egentligen var.

Inte heller premissen om de välinformerade konsumenterna håller. Tvärtom – en stor del av det offentliga uppdraget handlar om att hjälpa och stötta de som bäst behöver det, att utjämna klassamhällets skillnader och att förebygga olyckor som kan drabba den enskilde. I sådana situationer riktar sig tjänsterna till medborgare som ofta har ganska dåliga förutsättningar att göra välöverlagda val.

Alldeles för ofta riskerar valfriheten att gynna de starka. De som har tid att leta efter bra skolor åt sina barn, de som umgås med läkare som kan tipsa om kliniker och vårdcentraler, eller de som kan etablera sig som utförare på goda villkor – åt dem som har ska LOV varda givet.

Gunnar Wetterberg

[h/t Jan Milch]

Avec le temps

11 Jan, 2020 at 21:03 | Posted in Varia | Leave a comment

 

Avec le temps, va, tout s’en va
Et l’on se sent blanchi comme un cheval fourbu
Et l’on se sent glacé dans un lit de hasard
Et l’on se sent tout seul peut-être mais peinard
Et l’on se sent floué par les années perdues, alors vraiment
Avec le temps on n’aime plus

Economics — too important to be left to economists

11 Jan, 2020 at 14:25 | Posted in Economics | 2 Comments

aaBad economics underpinned the grand giveaways to the rich and the squeezing of welfare programs, sold the idea that the state is impotent and corrupt and the poor are lazy, and paved the way to the current stalemate of exploding inequality and angry inertia. Blinkered economics told us trade is good for everyone, and faster growth is everywhere. Blind economics missed the explosion in inequality all over the world, the increasing social fragmentation that came with it, and the impending environmental disaster, delaying action, perhaps irrevocably.

Two of last year’s ‘Nobel Prize’ winners in economics, Esther Duflo and Abhijit Banerjee, are back with a follow-up to their 2011 book Poor Economics. In the new book — Good Economics for Hard Times — they set out to show that although few people nowadays trust economists, there is a way to “make economics great again.” What has undermined the general public’s trust in economists is bad economics, and there has been, and still is, plenty of it (the authors take trade liberalisation, growth theory, migration, inequality and climate change as poignant examples.) The alternative, good, economics is — as was argued already in the earlier book — what results when economists work more like plumbers and “solve problems with a combination intuition grounded in science, some guesswork aided by experience, and a bunch of pure trial and error.”

Although yours truly agrees with most of the picture the authors give of present-day bad (mainstream) economics, I’m less convinced of their alternative.

Duflo and Banerjee think that economics should be based on evidence from randomised experiments and field studies. But to give up on ‘big ideas’ like political economy and institutional reform and instead go for solving more manageable problems the way plumbers do, is in my view not sufficient to move economics forward and make it a relevant and realist science. A plumber can fix minor leaks in your system, but if the whole system is rotten, something more than good old fashion plumbing is needed. The big social and economic problems we face today are not going to be solved by plumbers performing RCTs. We need to dig deeper than plumbers and make sure we get at the deep causal mechanisms behind the present stagnation of capitalism and the climate catastrophe it has landed us in.

There is also a rather disturbing kind of scientific naïveté in the Duflo-Banerjee approach to combatting socio-economic and environmental problems. The way they present their whole endeavour smacks of not so little ‘scientism’ where fighting problems becomes a question of applying ‘objective’ quantitative ‘techniques.’ But that can’t be the right way! Fighting problems like poverty and inequality is basically a question of changing the structures and institutions of our economies and societies.

Does it — really — take a model to beat a model?

10 Jan, 2020 at 11:05 | Posted in Economics | 3 Comments

A critique yours truly sometimes encounters is that as long as I cannot come up with some own alternative model to the failing mainstream models, I shouldn’t expect people to pay attention.

This is, however, to totally and utterly misunderstand the role of philosophy and methodology of economics!

As John Locke wrote in An Essay Concerning Human Understanding:

19557-004-21162361The Commonwealth of Learning is not at this time without Master-Builders, whose mighty Designs, in advancing the Sciences, will leave lasting Monuments to the Admiration of Posterity; But every one must not hope to be a Boyle, or a Sydenham; and in an Age that produces such Masters, as the Great-Huygenius, and the incomparable Mr. Newton, with some other of that Strain; ’tis Ambition enough to be employed as an Under-Labourer in clearing Ground a little, and removing some of the Rubbish, that lies in the way to Knowledge.

That’s what philosophy and methodology can contribute to economics — clearing obstacles to science by clarifying limits and consequences of choosing specific modelling strategies, assumptions, and ontologies.

unnameadIt takes a model to beat a model has to be one of the stupider things, in a pretty crowded field, to come out of economics. … I don’t get it. If a model is demonstrably wrong, that should surely be sufficient for rejection. I’m thinking of bridge engineers: ‘look I know they keep falling down but I’m gonna keep building em like this until you come up with a better way, OK?’

Jo Michell

Why all the fuzz about trade?

9 Jan, 2020 at 22:21 | Posted in Economics | 5 Comments

imagesThe share of US expenditure on imports is smaller than in most other countries. To a large extent, this reflects the fact that for a large country like the United States, a significant fraction of trade occurs intra- rather than internationally. This basic observation implies that that the welfare gains from international trade in the United States are smaller than in most other countries. Although magnitudes vary greatly depending on how one infers the shape of the US demand for foreign factor services, the estimates of gains from trade for the US economy that we review range from 2 to 8 percent of GDP.

Though such gains are nothing to spit at—they are an order of magnitude larger than the estimated gains from eliminating business cycle fluctuations—they may appear surprisingly small to some.

Arnaud Costinot & Andrés Rodríguez-Clare

And then we haven’t even started talking about the asymmetric distribution of pains and gains of international trade …

Is economics — really — predictable?

9 Jan, 2020 at 14:54 | Posted in Economics | 2 Comments

oskarAs Oskar Morgenstern noted already back in his 1928 classic Wirtschaftsprognose: Eine Untersuchung ihrer Voraussetzungen und Möglichkeiten, economic predictions and forecasts amount to little more than intelligent guessing.

Making forecasts and predictions obviously isn’t a trivial or costless activity, so why then go on with it?

The problems that economists encounter when trying to predict the future really underlines how important it is for social sciences to incorporate Keynes’s far-reaching and incisive analysis of induction and evidential weight in his seminal A Treatise on Probability (1921).

According to Keynes we live in a world permeated by unmeasurable uncertainty – not quantifiable stochastic risk – which often forces us to make decisions based on anything but ‘rational expectations.’ Keynes rather thinks that we base our expectations on the confidence or ‘weight’ we put on different events and alternatives. treatprobTo Keynes, ​expectations are a question of weighing probabilities by ‘degrees of belief,’ beliefs that often have preciously little to do with the kind of stochastic probabilistic calculations made by the rational agents as modelled by ‘modern’ social sciences. And often we “simply do not know.”

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 companies, governments, and central banks, continue with this more or less expensive, but obviously worthless, activity?

A part of the answer concerns ideology and apologetics. Forecasting is a non-negligible part of the labour market for (mainstream) economists, and so, of course, those in the business do not want to admit that they are occupied with worthless things (not to mention how hard it would be to sell the product with that kind of frank truthfulness). Governments, the finance sector and (central) banks also want to give the impression to customers and voters that they, so to say, have the situation under control (telling people that next years x will be 3.048 % makes wonders in that respect). Why else would anyone want to pay them or vote for them? These are sure not glamorous aspects of economics as a science, but as a scientist, it would be unforgivably dishonest to pretend that economics doesn’t also perform an ideological function in society.

Charles Taylor über die Suche nach der perfekten Gemeinschaft

7 Jan, 2020 at 18:47 | Posted in Politics & Society | Leave a comment

 

How to teach econometrics

6 Jan, 2020 at 14:54 | Posted in Statistics & Econometrics | 3 Comments

aWhen-I-tell-people-I-study-econometrics-1280x721Professor Swann (2019) seems implicitly to be endorsing the traditional theorem/proof style for teaching econometrics but with a few more theorems to be memorized. This style of teaching prepares students to join the monks in Asymptopia, a small pristine mountain village, where the monks read the tomes, worship the god of Consistency, and pray all day for the coming of the Revelation, when the estimates with an infinite sample will be revealed. Dirty limited real data sets with unknown properties are not allowed in Asymptopia, only hypothetical data with known properties. Not far away in the mountains is the village of Euphoria where celibate priests compose essays regarding human sexuality. Down on the plains is the very large city of Real Data, where applied economists torture dirty data until the data confess, providing the right signs and big t-values. Although Real Data is infinitely far from Asymptopia, these applied econometricians are fond of supporting the “Scientific” character of their work with quotations from the spiritual essays of the Monks of Asymptopia.

Ed Leamer

 

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