Hyman Minsky and the IS-LM obfuscation

26 Jan, 2023 at 16:38 | Posted in Economics | 3 Comments

As a young research stipendiate in the U.S. yours truly had the pleasure and privilege of having Hyman Minsky as a teacher. He was a great inspiration at the time. He still is.

The concepts which it is usual to ignore or deemphasize in interpreting Keynes — the cyclical perspective, the relations between investment and finance, and uncertainty, are the keys to an understanding of the full significance of his contribution …

miThe glib assumption made by Professor Hicks in his exposition of Keynes’s contribution that there is a simple, negatively sloped function, reflecting the productivity of increments to the stock of capital, that relates investment to the interest rate is a caricature of Keynes’s theory of investment … which relates the pace of investment not only to prospective yields but also to ongoing financial behavior …

The conclusion to our argument is that the missing step in the standard Keynesian theory was the explicit consideration of capitalist finance within a cyclical and speculative context. Once capitalist​ finance is introduced and the development of cash flows … during the various states of the economy is explicitly examined, then the full power of the revolutionary insights and the alternative frame of analysis that Keynes developed becomes evident …

The greatness of The General Theory was that Keynes visualized [the imperfections of the monetary-financial system] as systematic rather than accidental or perhaps incidental attributes of capitalism … Only a theory that was explicitly cyclical and overtly financial was capable of being useful …

If we are to believe Minsky — and I certainly think we should — then when people like Paul Krugman and other ‘New Keynesian’ critics of MMT and Post-Keynesian economics think of themselves as defending “the whole enterprise of Keynes/Hicks macroeconomic theory,” they are simply wrong since there is no such thing as a Keynes-Hicks macroeconomic theory!

There is nothing in the post-General Theory writings of Keynes that suggests that he considered Hicks’s IS-LM anywhere near a faithful rendering of his thoughts. In Keynes’s canonical statement of the essence of his theory in the 1937 QJE article there is nothing to even suggest that Keynes would have thought the existence of a Keynes-Hicks-IS-LM-theory anything but pure nonsense. So, of course,​ there can’t be any “vindication for the whole enterprise of Keynes/Hicks macroeconomic theory” — simply because “Keynes/Hicks” never existed.

To be fair to Hicks, we  shouldn’t forget that he returned to his IS-LM analysis in an article in 1980 — in Journal of Post Keynesian Economics — and self-critically wrote:

sir_john_hicksThe only way in which IS-LM analysis usefully survives — as anything more than a classroom gadget, to be superseded, later on, by something better — is in application to a particular kind of causal analysis, where the use of equilibrium methods, even a drastic use of equilibrium methods, is not inappropriate. I have deliberately interpreted the equilibrium concept, to be used in such analysis, in a very stringent manner (some would say a pedantic manner) not because I want to tell the applied economist, who uses such methods, that he is in fact committing himself to anything which must appear to him to be so ridiculous …

When one turns to questions of policy, looking toward the future instead of the past, the use of equilibrium methods is still more suspect … It may be hoped that, after the change in policy, the economy will somehow, at some time in the future, settle into what may be regarded, in the same sense, as a new equilibrium; but there must necessarily be a stage before that equilibrium is reached …

We now know that it is not enough to think of the rate of interest as the single link between the financial and industrial sectors of the economy; for that really implies that a borrower can borrow as much as he likes at the rate of interest charged, no attention being paid to the security offered. As soon as one attends to questions of security, and to the financial intermediation that arises out of them, it becomes apparent that the dichotomy between the two curves of the IS-LM diagram must not be pressed too hard.

In his 1937 paper, Hicks actually elaborates on four different models (where Hicks uses I to denote Total Income and Ix to denote Investment):

1) “Classical”: M = kI   Ix = C(i)   Ix = S(i,I)

2) Keynes’ “special” theory: M = L(i)   Ix = C(i)    I = S(I)

3) Keynes’ “general” theory: M = L(I, i)   Ix = C(i)   I = S(I)

4) The “generalized general” theory: M = L(I, i)   Ix =C(I, i)  Ix = S(I, i)

It is obvious from the way Krugman and other ‘New Keynesians’ draw their IS-LM curves that they are thinking in terms of model number 4 — and that is not even by Hicks considered a Keynes model! It is basically a loanable funds model, that belongs in the neoclassical camp and which you find reproduced in most mainstream textbooks.

Hicksian IS-LM? Maybe. Keynes? No way!

On gender and alcohol

23 Jan, 2023 at 18:15 | Posted in Statistics & Econometrics | 2 Comments

Breaking news! Using advanced multiple nonlinear regression models similar to those in recent news stories on alcohol and dairy and more than 3.6M observations from 1997 through 2012, I have found that drinking more causes people to turn into men!

Across people drinking 0-7 drinks per day, each drink per day causes the drinker’s probability of being a man to increase by 10.02 percentage points (z=302.2, p<0.0001). Need I say, profound implications for public health policy follow. The Lancet here I come!

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Chris Auld

Econometric identification sure is difficult …

When in Berlin … (personal)

22 Jan, 2023 at 21:29 | Posted in Varia | 1 Comment

If 1988 or 2023 doesn’t matter — visiting Café Einstein Stammhaus is a must.

jag på einstein-berlin1988

Pastoral

21 Jan, 2023 at 13:25 | Posted in Varia | 1 Comment

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Causal inferences — what Big Data cannot give us

20 Jan, 2023 at 13:25 | Posted in Statistics & Econometrics | Leave a comment

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The central problem with the present ‘Machine Learning’ and ‘Big Data’ hype is that so many — falsely — think that they can get away with analyzing real-world phenomena without any (commitment to) theory. But — data never speaks for itself.  Data by themselves are useless. Without a prior statistical set-up, there actually are no data at all to process.

Clever data-mining tricks are not enough to answer important scientific questions. Theory matters.

maIf we wanted highly probable claims, scientists would stick to​​ low-level observables and not seek generalizations, much less theories with high explanatory content. In this day​ of fascination with Big data’s ability to predict​ what book I’ll buy next, a healthy Popperian reminder is due: humans also want to understand and to explain. We want bold ‘improbable’ theories. I’m a little puzzled when I hear leading machine learners praise Popper, a realist, while proclaiming themselves fervid instrumentalists. That is, they hold the view that theories, rather than aiming at truth, are just instruments for organizing and predicting observable facts. It follows from the success of machine learning, Vladimir Cherkassy avers, that​ “realism is not possible.” This is very quick philosophy!

Quick indeed!

Systembrus

17 Jan, 2023 at 18:24 | Posted in Economics | 1 Comment

Brus (Noise) | Volante - En klokare & roligare världEn vanlig missuppfattning när det gäller oönskad variabilitet i bedömningar är att det inte spelar någon roll eftersom slumpfel tar ut varandra. Det stämmer att positiva och negativa fel i en bedömning av samma fall mer eller mindre tar ut varandra och vi kommer att diskutera mer i detalj hur denna omständighet kan utnyttjas för att minska bruset. Men brusiga system gör inte flera bedömningar av samma fall. De gör brusiga bedömningar av olika fall. Om en försäkring har för hög premie och en annan för låg kan prissättningen se korrekt ut “i genomsnitt”, men försäkringsbolaget har då begått två dyrbara misstag. Om två brottslingar som båda borde dömas till fem års fängelse får domar på tre respektive sju år kan inte någon “genomsnittlig” rättvisa sägas ha skipats. I brusiga system tar felen inte ut varandra. De läggs pa hög.

Victoria Chick (1936-2023) In Memoriam

16 Jan, 2023 at 16:17 | Posted in Economics | 2 Comments

Gresham's Law in Economics: Background to the CrisisSad news has reached us today. One of the leading Post Keynesian economists, Victoria Chick, passed away yesterday at the age of 86.

R.I.P.

Vague / E la nave va

15 Jan, 2023 at 17:10 | Posted in Varia | 1 Comment

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Is an economics degree really worth it?

13 Jan, 2023 at 16:36 | Posted in Economics | Comments Off on Is an economics degree really worth it?

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A science that doesn’t self-reflect on its own history and asks important methodological and science-theoretical questions about its own activity is in dire straits.

Already back in 1991, a commission chaired by Anne Krueger and including people like Kenneth Arrow, Edward Leamer, and Joseph Stiglitz, reported from their own experience “that it is an underemphasis on the ‘linkages’ between tools, both theory and econometrics, and ‘real world problems’ that is the weakness of graduate education in economics,” and that both students and faculty sensed “the absence of facts, institutional information, data, real-world issues, applications, and policy problems.” And in conclusion, they wrote that “graduate programs may be turning out a generation with too many idiot savants skilled in technique but innocent of real economic issues.”

Not much is different today. Economics — and economics education — is still in dire need of a remake.

More and more young economics students want to see a fundamental change in economics and how it’s taught. They want something other than the same old mainstream catechism. They don’t want to be force-fed with useless and harmfully irrelevant mainstream theories and models.

The 25 Best Econometrics Blogs

13 Jan, 2023 at 09:58 | Posted in Statistics & Econometrics | 1 Comment

Yours truly, of course, feels truly honored to find himself on the list of the world’s 25 best econometrics blogs.

1. Bruno Rodrigues

7. Eran Raviv Blog Statistics and Econometrics

9. How the (Econometric) Sausage is Made

14. Lars P Syll

Top Economics Blogs | LARS P. SYLLPålsson Syll received a Ph.D. in economic history in 1991 and a Ph.D. in economics in 1997, both at Lund University. He became an associate professor in economic history in 1995 and has since 2004 been a professor of social science at Malmö University. His primary research areas have been in the philosophy, history, and methodology of economics.

Desperado

11 Jan, 2023 at 22:39 | Posted in Varia | 1 Comment

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Identity politics and enlightenment

9 Jan, 2023 at 16:14 | Posted in Politics & Society | Comments Off on Identity politics and enlightenment

The Age of Identity PoliticsThere has developed in recent years an impassioned debate about the Enlightenment, which both supporters and critics present as a peculiarly European phenomenon. For the one, it is a demonstration of the greatness of Europe; for the other, a reminder that its ideals are tainted by racism and colonialism. Both miss the importance of the non-European world in shaping many of the ideas we associate with the Enlightenment. It was through the struggles of those denied equality and liberty by the elites in Europe and America that ideas of universalism were invested with meaning. It is the demise of that radical universalist tradition that has shaped the emergence of contemporary identity politics.

There have always been identitarian strands among antiracists, from 19th-century “Back to Africa” movements to Négritude in the 20th century. Only in the postwar world, however, have they come to dominate and to be seen as progressive. The reasons lie in a myriad of social and political developments, from the erosion of class politics, to the emergence of culture as the primary lens through which to understand social differences, to the growth of social pessimism, that have helped marginalise the universalist perspective.

The embrace of identity politics by the left has ironically opened the door for the reactionary right to reclaim its original inheritance, allowing racism to be rebranded as white identity politics. We have come full circle: the politics of identity that began as reactionary claims about a racial hierarchy has been regrasped by the reactionary right in the name of cultural difference.

Kenan Malik

Make Me Smile

8 Jan, 2023 at 21:58 | Posted in Varia | 1 Comment

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Econometric testing

8 Jan, 2023 at 14:09 | Posted in Statistics & Econometrics | 1 Comment

Debating econometrics and its shortcomings yours truly often gets the response from econometricians that “ok, maybe econometrics isn’t perfect, but you have to admit that it is a great technique for empirical testing of economic hypotheses.”

But is econometrics — really — such a great testing instrument?

ecokEconometrics is supposed to be able to test economic theories. But to serve as a testing device you have to make many assumptions, many of which themselves cannot be tested or verified. To make things worse, there are also only rarely strong and reliable ways of telling us which set of assumptions is to be preferred. Trying to test and infer causality from (non-experimental) data you have to rely on assumptions such as disturbance terms being ‘independent and identically distributed’; functions being additive, linear, and with constant coefficients; parameters being’ ‘invariant under intervention; variables being ‘exogenous’, ‘identifiable’, ‘structural and so on. Unfortunately, we are seldom or never informed of where that kind of ‘knowledge’ comes from, beyond referring to the economic theory that one is supposed to test. Performing technical tests is of course needed, but perhaps even more important is to know — as David Colander put it — “how to deal with situations where the assumptions of the tests do not fit the data.”

That leaves us in the awkward position of having to admit that if the assumptions made do not hold, the inferences, conclusions, and testing outcomes econometricians come up with simply do not follow from the data and statistics they use.

The central question is “how do we learn from empirical data?” Testing statistical/econometric models is one way, but we have to remember that the value of testing hinges on our ability to validate the — often unarticulated technical — basic assumptions on which the testing models build. If the model is wrong, the test apparatus simply gives us fictional values. There is always a strong risk that one puts a blind eye to some of those non-fulfilled technical assumptions that actually make the testing results — and the inferences we build on them — unwarranted.

Haavelmo’s probabilistic revolution gave econometricians their basic framework for testing economic hypotheses. It still builds on the assumption that the hypotheses can be treated as hypotheses about (joint) probability distributions and that economic variables can be treated as if pulled out of an urn as a random sample. But as far as I can see economic variables are nothing of that kind.

I still do not find any hard evidence that econometric testing uniquely has been able to “exclude a theory”. As Renzo Orsi put it: “If one judges the success of the discipline on the basis of its capability of eliminating invalid theories, econometrics has not been very successful.”

Most econometricians today … believe that the main objective of applied econometrics is the confrontation of economic theories with observable phenomena. This involves theory testing, for example testing monetarism or rational consumer behaviour. The econometrician’s task would be to find out whether a particular economic theory is true or not, using economic data and statistical tools. Nobody would say that this is easy. But is it possible? This question is discussed in Keuzenkamp and Magnus (1995). At the end of our paper we invited the readers to name a published paper that contains a test which, in their opinion, significantly changed the way economists think about some economic proposition … What happened? One Dutch colleague called me up and asked whether he could participate without having to accept the prize. I replied that he could, but he did not participate. Nobody else responded. Such is the state of current econometrics.

Jan Magnus

The econometric illusion

7 Jan, 2023 at 17:51 | Posted in Statistics & Econometrics | 11 Comments

What has always bothered me about the “experimentalist” school is the false sense of certainty it conveys. The basic idea is that if we have a “really good instrument” we can come up with “convincing” estimates of “causal effects” that are not “too sensitive to assumptions.” Elsewhere I have written  an extensive critique of this experimentalist perspective, arguing it presents a false panacea, andthat allstatistical inference relies on some untestable assumptions …

Maimonides Quote: Teach thy tongue to say 'I do not know,' and thou shalt  progress.Consider Angrist and Lavy (1999), who estimate the effect of class size on student performance by exploiting variation induced by legal limits. It works like this: Let’s say a law  prevents class size from exceeding. Let’s further assume a particular school has student cohorts that average about 90, but that cohort size fluctuates between, say, 84 and 96. So, if cohort size is 91–96 we end up with four classrooms of size 22 to 24, while if cohort size is 85–90 we end up with three classrooms of size 28 to 30. By comparing test outcomes between students who are randomly assigned to the small vs. large classes (based on their exogenous birth timing), we obtain a credible estimate of the effect of class size on academic performance. Their answer is that a ten-student reduction raises scores by about 0.2 to 0.3 standard deviations.

This example shares a common characteristic of natural experiment studies, which I think accounts for much of their popularity: At first blush, the results do seem incredibly persuasive. But if you think for awhile, you start to see they rest on a host of assumptions. For example, what if schools that perform well attract more students? In this case, incoming cohort sizes are not random, and the whole logic beaks down. What if parents who care most about education respond to large class sizes by sending their kids to a different school? What if teachers assigned to the extra classes offered in high enrollment years are not a random sample of all teachers?

Michael Keane

Keane’s critique of econometric ‘experimentalists’ gives a fair picture of some of the unfounded and exaggerated claims put forward in many econometric natural experiment studies. But — much of the critique really applies to econometrics in general, including the kind of ‘structural’ econometrics Keane himself favours!

The processes that generate socio-economic data in the real world cannot just be assumed to always be adequately captured by a probability measure. And, so, it cannot be maintained that it even should be mandatory to treat observations and data — whether cross-section, time series or panel data — as events generated by some probability model. The important activities of most economic agents do not usually include throwing dice or spinning roulette wheels. Data-generating processes — at least outside of nomological machines like dice and roulette wheels — are not self-evidently best modelled with probability measures.

When economists and econometricians — often uncritically and without arguments — simply assume that one can apply probability distributions from statistical theory to their own area of research, they are skating on thin ice. If you cannot show that data satisfies all the conditions of the probabilistic ‘nomological machine,’ then the statistical inferences made in mainstream economics lack sound foundations.

Statistical — and econometric — patterns should never be seen as anything other than possible clues to follow. Behind observable data, there are real structures and mechanisms operating, things that are — if we really want to understand, explain and (possibly) predict things in the real world — more important to get hold of than to simply correlate and regress observable variables.

Statistics cannot establish the truth value of a fact. Never has. Never will.

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