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Keynes on the 'devastating inconsistencies' of econometrics30 November, 2016 at 11:05 | Posted in Statistics & Econometrics | 7 Comments
In practice Prof. Tinbergen seems to be entirely indifferent whether or not his basic factors are independent of one another … But my mind goes back to the days when Mr. Yule sprang a mine under the contraptions of optimistic statisticians by his discovery of spurious correlation. In plain terms, it is evident that if what is really the same factor is appearing in several places under various disguises, a free choice of regression coefficients can lead to strange results. It becomes like those puzzles for children where you write down your age, multiply, add this and that, subtract something else, and eventually end up with the number of the Beast in Revelation.
Prof. Tinbergen explains that, generally speaking, he assumes that the correlations under investigation are linear … I have not discovered any example of curvilinear correlation in this book, and he does not tell us what kind of evidence would lead him to introduce it. If, as he suggests above, he were in such cases to use the method of changing his linear coefficients from time to time, it would certainly seem that quite easy manipulation on these lines would make it possible to fit any explanation to any facts. Am I right in thinking that the uniqueness of his results depends on his knowing beforehand that the correlation curve must be a particular kind of function, whether linear or some other kind ?
Apart from this, one would have liked to be told emphatically what is involved in the assumption of linearity. It means that the quantitative effect of any causal factor on the phenomenon under investigation is directly proportional to the factor's own magnitude … But it is a very drastic and usually improbable postulate to suppose that all economic forces are of this character, producing independent changes in the phenomenon under investigation which are directly proportional to the changes in themselves ; indeed, it is ridiculous. Yet this is what Prof. Tinbergen is throughout assuming …
Keynes' comprehensive critique of econometrics and the assumptions it is built around — completeness, measurability, indepencence, homogeneity, and linearity — is still valid today.
Most work in econometrics is made on the assumption that the researcher has a theoretical model that is 'true.' But — to think that we are being able to construct a model where all relevant variables are included and correctly specify the functional relationships that exist between them, is not only a belief without support, it is a belief impossible to support.
The theories we work with when building our econometric regression models are insufficient. No matter what we study, there are always some variables missing, and we don't know the correct way to functionally specify the relationships between the variables.
Every econometric model constructed is misspecified. There are always an endless list of possible variables to include, and endless possible ways to specify the relationships between them. So every applied econometrician comes up with his own specification and 'parameter' estimates. The econometric Holy Grail of consistent and stable parameter-values is nothing but a dream.
A rigorous application of econometric methods in economics really presupposes that the phenomena of our real world economies are ruled by stable causal relations between variables. Parameter-values estimated in specific spatio-temporal contexts are presupposed to be 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.
The theoretical conditions that have to be fulfilled for econometrics to really work are nowhere even closely met in reality. Making outlandish statistical assumptions does not provide a solid ground for doing relevant social science and economics. Although econometrics have become the most used quantitative methods in economics today, it's still a fact that the inferences made from them are as a rule invalid.
Econometrics is basically a deductive method. Given the assumptions it delivers deductive inferences. The problem, of course, is that we will never completely know when the assumptions are right. Conclusions can only be as certain as their premises — and that also applies to econometrics.
Three suggestions to 'save' econometrics29 November, 2016 at 11:33 | Posted in Economics, Statistics & Econometrics | 6 Comments
Reading an applied econometrics paper could leave you with the impression that the economist (or any social science researcher) first formulated a theory, then built an empirical test based on the theory, then tested the theory. But in my experience what generally happens is more like the opposite: with some loose ideas in mind, the econometrician runs a lot of different regressions until they get something that looks plausible, then tries to fit it into a theory (existing or new) … Statistical theory itself tells us that if you do this for long enough, you will eventually find something plausible by pure chance!
This is bad news because as tempting as that final, pristine looking causal effect is, readers have no way of knowing how it was arrived at. There are several ways I've seen to guard against this:
(1) Use a multitude of empirical specifications to test the robustness of the causal links, and pick the one with the best predictive power …
(2) Have researchers submit their paper for peer review before they carry out the empirical work, detailing the theory they want to test, why it matters and how they're going to do it. Reasons for inevitable deviations from the research plan should be explained clearly in an appendix by the authors and (re-)approved by referees.
(3) Insist that the paper be replicated. Firstly, by having the authors submit their data and code and seeing if referees can replicate it (think this is a low bar? Most empirical research in 'top' economics journals can't even manage it). Secondly — in the truer sense of replication — wait until someone else, with another dataset or method, gets the same findings in at least a qualitative sense. The latter might be too much to ask of researchers for each paper, but it is a good thing to have in mind as a reader before you are convinced by a finding.
All three of these should, in my opinion, be a prerequisite for research that uses econometrics …
Naturally, this would result in a lot more null findings and probably a lot less research. Perhaps it would also result in fewer attempts at papers which attempt to tell the entire story: that is, which go all the way from building a new model to finding (surprise!) that even the most rigorous empirical methods support it.
Good suggestions, but unfortunately there are many more deep problems with econometrics that have to be 'solved.'
In econometrics one often gets the feeling that many of its practitioners think of it as a kind of automatic inferential machine: input data and out comes causal knowledge. This is like pulling a rabbit from a hat. Great — but first you have to put the rabbit in the hat. And this is where assumptions come in to the picture. The assumption of imaginary 'superpopulations' is one of the many dubious assumptions used in modern econometrics.
Misapplication of inferential statistics to non-inferential situations is a non-starter for doing proper science. And when choosing which models to use in our analyses, we cannot get around the fact that the evaluation of our hypotheses, explanations, and predictions cannot be made without reference to a specific statistical model or framework. The probabilistic-statistical inferences we make from our samples decisively depends on what population we choose to refer to. The reference class problem shows that there usually are many such populations to choose from, and that the one we choose decides which probabilities we come up with and a fortiori which predictions we make. Not consciously contemplating the relativity effects this choice of 'nomological-statistical machines' have, is probably one of the reasons econometricians have a false sense of the amount of uncertainty that really afflicts their models.
As economists and econometricians we have to confront the all-important question of how to handle uncertainty and randomness. Should we define randomness with probability? If we do, we have to accept that to speak of randomness we also have to presuppose the existence of nomological probability machines, since probabilities cannot be spoken of – and actually, to be strict, do not at all exist – without specifying such system-contexts. Accepting Haavelmo's domain of probability theory and sample space of infinite populations – just as Fisher's 'hypothetical infinite population,' von Mises's 'collective' or Gibbs's 'ensemble' – also implies that judgments are made on the basis of observations that are actually never made! Infinitely repeated trials or samplings never take place in the real world. So that cannot be a sound inductive basis for a science with aspirations of explaining real-world socio-economic processes, structures or events. It's not tenable.
Economists — and econometricians — have (uncritically and often without arguments) come to simply assume that one can apply probability distributions from statistical theory on their own area of research. However, there are fundamental problems arising when you try to apply statistical models outside overly simple nomological machines like coin tossing and roulette wheels.
Of course one could arguably treat our observational or experimental data as random samples from real populations. But probabilistic econometrics does not content itself with that kind of populations. Instead it creates imaginary populations of 'parallel universes' and assume that our data are random samples from that kind of populations. But this is actually nothing but hand-waving! Doing econometrics it's always wise to remember C. S. Peirce's remark that universes are not as common as peanuts …
'Teaching-to-the-test' — fel väg framåt för svensk skola28 November, 2016 at 19:13 | Posted in Education & School | Comments Off on 'Teaching-to-the-test' — fel väg framåt för svensk skola
Det är värt att notera att kunskapsnedgången i de internationella undersökningarna överhuvudtaget inte avspeglas i resultaten på de nationella proven (jag är medveten om att axlarna i figuren nedan inte är optimalt skalade). En tolkning av detta är att undervisningen idag är så inriktad på proven att eleverna trots fallande underliggande kunskapsnivå ändå lyckas rätt hyfsat på dem. När eleverna ställs inför nya typer av uppgifter står de emellertid sig slätt. Detta skulle i så fall tyda på att provens utformning gör dem lätta att genomskåda och lära sig prestera bra på, utan att eleverna tillägnat sig djupare ämneskunskaper och ämnesförståelse.
Dessa siffror visar vad som borde vara allmänt känt inom utbildningsforskningen; utvärderingssystemet påverkar verksamheten i betydligt högre grad än styrdokument och allmänna målsättningar … Elever tenderar att bli bättre på just den typ av prov som används som utvärderingsinstrument, men inte nödvändigtvis på andra typer av prov. Även om ett större inslag av "teaching-to-the-test" inte definitionsmässigt är dåligt, så tyder inte den svenska erfarenheten på att det är en självklar väg till bättre resultat.
Visst är det möjligt att utvecklingen hade varit ännu sämre utan de nationella provens ökade betydelse, men samtidigt finns det en uppenbar möjlighet att motsatsen är sann.
The elite illusion28 November, 2016 at 15:25 | Posted in Statistics & Econometrics | Comments Off on The elite illusion
The results reported here suggest that an exam school education produces only scattered gains for applicants, even among students with baseline scores close to or above the mean in the target school. Because the exam school experience is associated with sharp increases in peer achievement, these results weigh against the importance of peer effects in the education production function …
Of course, test scores and peer effects are only part of the exam school story. It may be that preparation for exam school entrance is itself worth-while … The many clubs and activities found at some exam schools may expose students to ideas and concepts not easily captured by achievement tests or our post-secondary outcomes. It is also possible that exam school graduates earn higher wages, a question we plan to explore in future work. Still, the estimates reported here suggest that any labor market gains are likely to come through channels other than peer composition and increased cognitive achievement …
Our results are also relevant to the economic debate around school quality and school choice … As with the jump in house prices at school district boundaries, heavy rates of exam school oversubscription suggest that parents believe peer composition matters a great deal for their children's welfare. The fact that we find little support for causal peer effects suggests that parents either mistakenly equate attractive peers with high value added, or that they value exam schools for reasons other than their impact on learning. Both of these scenarios reduce the likelihood that school choice in and of itself has strong salutary demand-side effects in education production.
Results based on one of the latest fads in econometrics — regression discontinuity design. If unfamiliar with the 'technique,' here's a video giving some of the basics:
Serenity (personal)28 November, 2016 at 13:44 | Posted in Varia | Comments Off on Serenity (personal)
[h/t Eric Schüldt]
The Economist — Economics prone to fads and methodological crazes27 November, 2016 at 18:49 | Posted in Economics | 2 Comments
When a hot new tool arrives on the scene, it should extend the frontiers of economics and pull previously unanswerable questions within reach. What might seem faddish could in fact be economists piling in to help shed light on the discipline's darkest corners. Some economists, however, argue that new methods also bring new dangers; rather than pushing economics forward, crazes can lead it astray, especially in their infancy …
A paper by Angus Deaton, a Nobel laureate and expert data digger, and Nancy Cartwright, an economist (sic!) at Durham University, argues that randomised control trials, a current darling of the discipline, enjoy misplaced enthusiasm. RCTs involve randomly assigning a policy to some people and not to others, so that researchers can be sure that differences are caused by the policy. Analysis is a simple comparison of averages between the two. Mr Deaton and Ms Cartwright have a statistical gripe; they complain that researchers are not careful enough when calculating whether two results are significantly different from one another. As a consequence, they suspect that a sizeable portion of published results in development and health economics using RCTs are "unreliable".
With time, economists should learn when to use their shiny new tools. But there is a deeper concern: that fashions and fads are distorting economics, by nudging the profession towards asking particular questions, and hiding bigger ones from view. Mr Deaton's and Ms Cartwright's fear is that RCTs yield results while appearing to sidestep theory, and that "without knowing why things happen and why people do things, we run the risk of worthless causal ('fairy story') theorising, and we have given up on one of the central tasks of economics." Another fundamental worry is that by offering alluringly simple ways of evaluating certain policies, economists lose sight of policy questions that are not easily testable using RCTs, such as the effects of institutions, monetary policy or social norms.
Still no. 1 !27 November, 2016 at 11:58 | Posted in Varia | 1 Comment
Om ekonomiskt vetande — Fronesis nr 54-5526 November, 2016 at 15:37 | Posted in Economics | Comments Off on Om ekonomiskt vetande — Fronesis nr 54-55
Efter den globala finanskrisen 2008 har den ekonomiska vetenskapen hamnat i blickfånget. Studentrörelser och heterodoxa ekonomer har kritiserat det dominerande ekonomiska paradigmet och krävt ökad pluralism. Den senaste tidens politiska utveckling har blottat nyliberalismens brister och aktualiserat frågan om dess koppling till den ekonomiska vetenskapen. I Fronesis nr 54–55 fördjupar vi oss i det ekonomiska vetandets förutsättningar.
Vänstern har länge inriktat sitt samhällsteoretiska och politiska intresse mot kulturella och symboliska aspekter av makt och dominans, men har mer eller mindre lämnat det ekonomiska fältet därhän. Med Fronesis nr 54–55 vill vi röra oss bortom en simpel kritik av nationalekonomin och fördjupa förståelsen av villkoren för det ekonomiska vetandet. Numret introducerar för en svensk publik en rad centrala samtida teoretiker som belyser frågorna ur olika perspektiv.
Kajsa Borgnäs och Anders Hylmö: Det ekonomiska vetandet i förändring Ladda ner som PDF
Anders Hylmö: Den moderna nationalekonomin som vetenskaplig stil och disciplin
Dimitris Milonakis: Lärdomar från krisen
Marion Fourcade, Étienne Ollion och Yann Algan: Ekonomernas överlägsenhet
Kajsa Borgnäs: Utanför boxen, eller Vad är heterodox nationalekonomi?
Lars Pålsson Syll: Tony Lawson och kritiken av den nationalekonomiska vetenskapen – en introduktion
Tony Lawson: Den heterodoxa ekonomins natur
Josef Taalbi: Realistisk ekonomisk teori?
Erik Bengtsson: Den heterodoxa nationalekonomins materiella förutsättningar
Julie A. Nelson: Genusmetaforer och nationalekonomi
Linda Nyberg: Nyliberalism, politik och ekonomi
Philip Mirowski: Den politiska rörelsen som inte vågade säga sitt namn
Jason Read: En genealogi över homo oeconomicus
Kajsa Borgnäs: Den vetenskapliga ekonomins politiska makt
Daniel Hirschman och Elizabeth Popp Berman: Skapar nationalekonomer politik?
Peter Gerlach, Marika Lindgren Åsbrink och Ola Pettersson, intervjuade av Daniel Mathisen: Allt annat lika
The use of mathematics in physics and economics26 November, 2016 at 12:31 | Posted in Economics | Comments Off on The use of mathematics in physics and economics
My idea is to examine the most well-known works of a selection of the most famous neoclassical economists in the period from 1945 to the present.
My survey of well-known works by four famous mathematical neoclassical economists (Samuelson, Arrow, Debreu, Prescott) who all won the Nobel Prize for economics, has not revealed any precise explanations or successful predictions. This supports my conjecture that the use of mathematics in mainstream (or neoclassical) economics has not produced any precise explanations or successful predictions. This, I would claim, is the main difference between neoclassical economics and physics, where both precise explanations and successful predictions have often been obtained by the use of mathematics.
What we do in life echoes in eternity25 November, 2016 at 18:45 | Posted in Varia | Comments Off on What we do in life echoes in eternity
In science courage is to follow the motto of enlightenment and Kant's dictum — Sapere Aude! To use your own understanding, having the the courage to think for yourself and question 'received opinion,' authority or orthodoxy.
In our daily lives courage is a capability to confront fear, as when in front of the powerful and mighty, not to step back, but stand up for one's rights not to be humiliated or abused in any ways by the rich and powerful.
Dignity, a better life, or justice and rule of law, are things worth fighting for. Not to step back creates courageous acts that stay in our memories and means something. As when Rosa Parks sixty years ago, on December 1, 1955, in Montgomery, Alabama, refused to give up her seat to make room for a white passenger.
Courage is to do the right thing in spite of danger and fear. To keep on even if opportunities to turn back are given. Like in the great stories. The ones where people have lots of chances of turning back — but don't.
As when Sir Nicholas Winton organised the rescue of 669 children destined for Nazi concentration camps during World War II.
Or as when Ernest Shackleton, in April 1916, aboard the small boat 'James Caird', spent 16 days crossing 1,300 km of ocean to reach South Georgia, then trekked across the island to a whaling station, and finally could rescue the remaining men from the crew of 'Endurance' left on the Elephant Island.
Not a single member of the expedition died.
What we do in life echoes in eternity.
1980s nostalgia (personal)25 November, 2016 at 17:16 | Posted in Varia | 1 Comment
My youngest — born in 1999 — asked me the other day what kind of music her dad listened to back in the swinging 80s. So here, Linnea, is a little taste of paternal nostalgia:
Why IS-LM doesn't capture Keynes' approach to the economy25 November, 2016 at 12:39 | Posted in Economics | 2 Comments
Suppose workers are unemployed. As a result, although willing to work even at lower wages, they are unable to buy consumption goods. As a result, firms are unable to sell those goods if they produced them. So they do not employ the workers who, as a consequence, do not have the wages to buy the consumption goods. The economy is caught in a vicious cycle of deficient demand. According to the IS/LM framework, this would lead to a fall in prices and wages, raise real balances and boost demand. But falling prices and wages might have the effect of both reducing effective demand and confidence, deepening rather than resolving the problem of unemployment.
These considerations raise serious doubts whether the IS/LM approach, despite being the standard representation, fully captures the Keynesian approach to the economy other than in name …
The appeal of the IS/LM lay not only in its formalisation of what is falsely taken to be Keynes' specific contribution but also in compromising with a Walrasian approach to the economy.
Ben Fine and Ourania Dimakou have some further interesting references for those wanting to dwell upon the question of how much Keynes really there is in Hicks's IS-LM model.
My own view is that IS-LM doesn't adequately reflect the width and depth of Keynes's insights on the workings of modern market economies for the following six reasons:
1 Almost nothing in the post-General Theory writings of Keynes suggests him considering Hicks's IS-LM anywhere near a faithful rendering of his thought. In Keynes's canonical statement of the essence of his theory — in the famous 1937 Quarterly Journal of Economics 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. John Hicks, the man who invented IS-LM in his 1937 Econometrica review of Keynes' General Theory — "Mr. Keynes and the 'Classics'. A Suggested Interpretation" — returned to it in an article in 1980 — "IS-LM: an explanation" — in Journal of Post Keynesian Economics. Self-critically he wrote that "the 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." What Hicks acknowledges in 1980 is basically that his original IS-LM model ignored significant parts of Keynes' theory. IS-LM is inherently a temporary general equilibrium model. However — much of the discussions we have in macroeconomics is about timing and the speed of relative adjustments of quantities, commodity prices and wages — on which IS-LM doesn't have much to say.
2 IS-LM forces to a large extent the analysis into a static comparative equilibrium setting that doesn't in any substantial way reflect the processual nature of what takes place in historical time. To me Keynes's analysis is in fact inherently dynamic — at least in the sense that it was based on real historic time and not the logical-ergodic-non-entropic time concept used in most neoclassical model building. And as Niels Bohr used to say — thinking is not the same as just being logical …
3 IS-LM reduces interaction between real and nominal entities to a rather constrained interest mechanism which is far too simplistic for analyzing complex financialised modern market economies.
4 IS-LM gives no place for real money, but rather trivializes the role that money and finance play in modern market economies. As Hicks, commenting on his IS-LM construct, had it in 1980 — "one did not have to bother about the market for loanable funds." From the perspective of modern monetary theory, it's obvious that IS-LM to a large extent ignores the fact that money in modern market economies is created in the process of financing — and not as IS-LM depicts it, something that central banks determine.
5 IS-LM is typically set in a current values numéraire framework that definitely downgrades the importance of expectations and uncertainty — and a fortiori gives too large a role for interests as ruling the roost when it comes to investments and liquidity preferences. In this regard it is actually as bad as all the modern microfounded Neo-Walrasian-New-Keynesian models where Keynesian genuine uncertainty and expectations aren't really modelled. Especially the two-dimensionality of Keynesian uncertainty — both a question of probability and "confidence" — has been impossible to incorporate into this framework, which basically presupposes people following the dictates of expected utility theory (high probability may mean nothing if the agent has low "confidence" in it). Reducing uncertainty to risk — implicit in most analyses building on IS-LM models — is nothing but hand waving. 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. To 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 modeled by "modern" social sciences. And often we "simply do not know."
6 IS-LM not only ignores genuine uncertainty, but also the essentially complex and cyclical character of economies and investment activities, speculation, endogenous money, labour market conditions, and the importance of income distribution. And as Axel Leijonhufvud so eloquently notes on IS-LM economics — "one doesn't find many inklings of the adaptive dynamics behind the explicit statics." Most of the insights on dynamic coordination problems that made Keynes write General Theory are lost in the translation into the IS-LM framework.
Given this, it's difficult not agree with Fine and Dimakou. The IS/LM approach doesn't capture Keynes' approach to the economy other than in name.
Endogenous growth theory — a crash course24 November, 2016 at 17:11 | Posted in Economics | 2 Comments
Årets dumstrut i Malmös politik23 November, 2016 at 22:26 | Posted in Education & School | Comments Off on Årets dumstrut i Malmös politik
MALMÖ. Klagomålen på den kommunala förskolan har mer än fördubblats de senaste två åren, men det oroar inte Rose-Marie Carlsson (S), ordförande i förskolenämnden.
– En ökning av klagomålen behöver inte betyda att det blivit sämre. Tvärtom, säger hon. Omorganisationen 2013, då förskolan fick egen nämnd och förvaltning, innebar även ett nytt sätt att arbeta med klagomålshantering.
–Det är enklare nu för vårdnadshavare att lämna in klagomål, bland annat kan det göras direkt på Malmö stads hemsida, säger Rose-Marie Carlsson …
Rose-Marie Carlsson menar att ökningen av antalet klagomål egentligen är något positivt – det bevisar att klagomålshanteringen fungerar bättre än innan omorganisationen 2013.
–Man måste komma ihåg att vi gått från tio stadsdelar med tio olika sätt att hantera dessa frågor till ett enda sätt att arbeta. Klagomålshanteringen är en del av vårt kvalitetsarbete, och vi arbetar mer systematiskt nu och följer upp klagomål på ett helt annat sätt än tidigare. Vi har bättre kontroll och tillsyn nu. Jag är mer bekymrad över de enheter där det inte anmälts några klagomål, där kan man misstänka bristande rutiner, säger hon.
Och detta grodors plums och ankors plask ska man behöva läsa år 2016. Herre du milde! Man tager sig för pannan. Sällan eller aldrig har väl ett så urbota löjligt försök att prata bort kritik hörts.
What is ergodicity?23 November, 2016 at 10:27 | Posted in Economics | 2 Comments
Why are election polls often inaccurate? Why is racism wrong? Why are your assumptions often mistaken? The answers to all these questions and to many others have a lot to do with the non-ergodicity of human ensembles. Many scientists agree that ergodicity is one of the most important concepts in statistics. So, what is it?
Suppose you are concerned with determining what the most visited parks in a city are. One idea is to take a momentary snapshot: to see how many people are this moment in park A, how many are in park B and so on. Another idea is to look at one individual (or few of them) and to follow him for a certain period of time, e.g. a year. Then, you observe how often the individual is going to park A, how often he is going to park B and so on.
Thus, you obtain two different results: one statistical analysis over the entire ensemble of people at a certain moment in time, and one statistical analysis for one person over a certain period of time. The first one may not be representative for a longer period of time, while the second one may not be representative for all the people.
The idea is that an ensemble is ergodic if the two types of statistics give the same result. Many ensembles, like the human populations, are not ergodic.
The importance of ergodicity becomes manifest when you think about how we all infer various things, how we draw some conclusion about something while having information about something else. For example, one goes once to a restaurant and likes the fish and next time he goes to the same restaurant and orders chicken, confident that the chicken will be good. Why is he confident? Or one observes that a newspaper has printed some inaccurate information at one point in time and infers that the newspaper is going to publish inaccurate information in the future. Why are these inferences ok, while others such as "more crimes are committed by black persons than by white persons, therefore each individual black person is not to be trusted" are not ok?
The answer is that the ensemble of articles published in a newspaper is more or less ergodic, while the ensemble of black people is not at all ergodic. If one searches how many mistakes appear in an entire newspaper in one issue, and then searches how many mistakes one news editor does over time, one finds the two results almost identical (not exactly, but nonetheless approximately equal). However, if one takes the number of crimes committed by black people in a certain day divided by the total number of black people, and then follows one random-picked black individual over his life, one would not find that, e.g. each month, this individual commits crimes at the same rate as the crime rate determined over the entire ensemble. Thus, one cannot use ensemble statistics to properly infer what is and what is not probable that a certain individual will do.
Or take an even clearer example: In an election each party gets some percentage of votes, party A gets a%, party B gets b% and so on. However, this does not mean that over the course of their lives each individual votes with party A in a% of elections, with B in b% of elections and so on …
A similar problem is faced by scientists in general when they are trying to infer some general statement from various particular experiments. When is a generalization correct and when it isn't? The answer concerns ergodicity. If the generalization is done towards an ergodic ensemble, then it has a good chance of being correct.
Paul Samuelson once famously claimed that the "ergodic hypothesis" is essential for advancing economics from the realm of history to the realm of science. But is it really tenable to assume — as Samuelson and most other mainstream economists — that ergodicity is essential to economics?
In this video Ole Peters shows why ergodicity is such an important concept for understanding the deep fundamental flaws of mainstream economics:
Sometimes ergodicity is mistaken for stationarity. But although all ergodic processes are stationary, they are not equivalent.
Let's say we have a stationary process. That does not guarantee that it is also ergodic. The long-run time average of a single output function of the stationary process may not converge to the expectation of the corresponding variables — and so the long-run time average may not equal the probabilistic (expectational) average. Say we have two coins, where coin A has a probability of 1/2 of coming up heads, and coin B has a probability of 1/4 of coming up heads. We pick either of these coins with a probability of 1/2 and then toss the chosen coin over and over again. Now let H1, H2, … be either one or zero as the coin comes up heads or tales. This process is obviously stationary, but the time averages — [H1 + … + Hn]/n — converges to 1/2 if coin A is chosen, and 1/4 if coin B is chosen. Both these time averages have a probability of 1/2 and so their expectational average is 1/2 x 1/2 + 1/2 x 1/4 = 3/8, which obviously is not equal to 1/2 or 1/4. The time averages depend on which coin you happen to choose, while the probabilistic (expectational) average is calculated for the whole "system" consisting of both coin A and coin B.
In an ergodic system time is irrelevant and has no direction. Nothing changes in any significant way; at most you will see some short-lived fluctuations. An ergodic system is indifferent to its initial conditions: if you re-start it, after a little while it always falls into the same equilibrium behavior.
For example, say I gave 1,000 people one die each, had them roll their die once, added all the points rolled, and divided by 1,000. That would be a finite-sample average, approaching the ensemble average as I include more and more people.
Now say I rolled a die 1,000 times in a row, added all the points rolled and divided by 1,000. That would be a finite-time average, approaching the time average as I keep rolling that die.
One implication of ergodicity is that ensemble averages will be the same as time averages. In the first case, it is the size of the sample that eventually removes the randomness from the system. In the second case, it is the time that I'm devoting to rolling that removes randomness. But both methods give the same answer, within errors. In this sense, rolling dice is an ergodic system.
I say "in this sense" because if we bet on the results of rolling a die, wealth does not follow an ergodic process under typical betting rules. If I go bankrupt, I'll stay bankrupt. So the time average of my wealth will approach zero as time passes, even though the ensemble average of my wealth may increase.
A precondition for ergodicity is stationarity, so there can be no growth in an ergodic system. Ergodic systems are zero-sum games: things slosh around from here to there and back, but nothing is ever added, invented, created or lost. No branching occurs in an ergodic system, no decision has any consequences because sooner or later we'll end up in the same situation again and can reconsider. The key is that most systems of interest to us, including finance, are non-ergodic.
Mainstream economics — sacrificing realism at the altar of mathematical purity22 November, 2016 at 18:05 | Posted in Economics | 6 Comments
Economists are too detached from the real world and have failed to learn from the financial crisis, insisting on using mathematical models which do not reflect reality, according to the Bank of England's chief economist Andy Haldane.
The public has lost faith in economists since the credit crunch, he said, but the profession has failed to thoroughly re-examine its failings to come up with a new model of operating.
"The various reports into the economic costs of the UK leaving the EU most likely fell at the same hurdle. They are written, in the main, by the elite for the elite," said Mr Haldane, writing the foreword to a new book, called 'The Econocracy: the perils of leaving economics to the experts' …
The chief economist said that the Great Depression of the 1930s resulted in a major overhaul of economic thinking, led by John Maynard Keynes, who emerged "as the most influential economist of the twentieth century".
But the recent financial crisis and slow recovery has not yet prompted this great re-thinking …
For now, economists need to focus on reviewing their models, accepting a diversify of thought rather than one solid orthodoxy, and on communicating more clearly.
Economists should focus on other disciplines as well as maths, he said.
"Mainstream economic models have sacrificed too much realism at the altar of mathematical purity. Their various simplifying assumptions have served aesthetic rather than practical ends," Mr Haldane wrote.
"As a profession, economics has become too much of a methodological monoculture. And that lack of intellectual diversity cost the profession dear when the single crop failed spectacularly during the crisis."
The rebel who blew up macroeconomics22 November, 2016 at 15:18 | Posted in Economics | 1 Comment
Paul Romer says he really hadn't planned to trash macroeconomics as a math-obsessed pseudoscience. Or infuriate countless colleagues. It just sort of happened …
The upshot was "The Trouble With Macroeconomics," a scathing critique that landed among Romer's peers like a grenade. In a time of febrile politics, with anti-establishment revolts breaking out everywhere, faith in economists was already ebbing: They got blamed for failing to see the Great Recession coming and, later, to suggest effective remedies. Then, along came one of the leading practitioners of his generation, to say that the skeptics were onto something.
"For more than three decades, macroeconomics has gone backwards," the paper began. Romer closed out his argument, some 20 pages later, by accusing a cohort of economists of drifting away from science, more interested in preserving reputations than testing their theories against reality, "more committed to friends than facts." In between, he offers a wicked parody of a modern macro argument: "Assume A, assume B, … blah blah blah … and so we have proven that P is true." …
Romer said he hopes at least to have set an example, for younger economists, of how scientific inquiry should proceed — on Enlightenment lines. No authority-figures should command automatic deference, or be placed above criticism, and voices from outside the like-minded group shouldn't be ignored. He worries that those principles are at risk, well beyond his own field … And at the deepest level, he thinks it's a misunderstanding of science that has sent so many economists down the wrong track. "Essentially, their belief was that math could tell you the deep secrets of the universe," he said.
'Post-real' macroeconomics — three decades of intellectual regress22 November, 2016 at 10:40 | Posted in Economics | 1 Comment
Macroeconomists got comfortable with the idea that fluctuations in macroeconomic aggregates are caused by imaginary shocks, instead of actions that people take, after Kydland and Prescott (1982) launched the real business cycle (RBC) model …
In response to the observation that the shocks are imaginary, a standard defence invokes Milton Friedman's (1953) methodological assertion from unnamed authority that "the more significant the theory, the more unrealistic the assumptions." More recently, "all models are false" seems to have become the universal hand-wave for dismissing any fact that does not conform to the model that is the current favourite.
The noncommittal relationship with the truth revealed by these methodological evasions and the "less than totally convinced …" dismissal of fact goes so far beyond post-modern irony that it deserves its own label. I suggest "post-real."
There are many kinds of useless economics held in high regard within the mainstream economics establishment today. Few are less deserved than the post-real macroeconomic theory — mostly connected with Finn Kydland, Robert Lucas, Edward Prescott and Thomas Sargent — called RBC.
In Chicago economics one is cultivating the view that scientific theories has nothing to do with truth. Constructing theories and building models is not even considered an activity wth the intent of approximating truth. For Chicago economists it is only an endeavour to organize their thoughts in a 'useful' manner.
What a handy view of science!
What these defenders of scientific storytelling 'forget' is that potential explanatory power achieved in thought experimental models is not enough for attaining real explanations. Model explanations are at best conjectures, and whether they do or do not explain things in the real world is something we have to test. To just believe that you understand or explain things better with thought experiments is not enough.
Without a warranted export certificate to the real world, model explanations are pretty worthless. Proving things in models is not enough — not even after having put 'New Keynesian' sticky-price DSGE lipstick on the RBC pig.
Truth is an important concept in real science — and models based on meaningless calibrated 'facts' and 'assumptions' with unknown truth value are poor substitutes.
Public debt should not be zero. Ever!22 November, 2016 at 09:26 | Posted in Economics | 3 Comments
Nation states borrow to provide public capital: For example, rail networks, road systems, airports and bridges. These are examples of large expenditure items that are more efficiently provided by government than by private companies.
The benefits of public capital expenditures are enjoyed not only by the current generation of people, who must sacrifice consumption to pay for them, but also by future generations who will travel on the rail networks, drive on the roads, fly to and from the airports and drive over the bridges that were built by previous generations. Interest on the government debt is a payment from current taxpayers, who enjoy the fruits of public capital, to past generations, who sacrificed consumption to provide that capital.
To maintain the roads, railways, airports and bridges, the government must continue to invest in public infrastructure. And public investment should be financed by borrowing, not from current tax revenues.
Investment in public infrastructure was, on average, equal to 4.3% of GDP in the period from 1948 through 1983. It has since fallen to 1.6% of GDP. There is a strong case to be made for increasing investment in public infrastructure. First, the public capital that was constructed in the post WWII period must be maintained in order to allow the private sector to function effectively. Second, there is a strong case for the construction of new public infrastructure to promote and facilitate future private sector growth.
The debt raised by a private sector company should be strictly less than the value of assets, broadly defined. That principle does not apply to a nation state. Even if government provided no capital services, the value of its assets or liabilities should not be zero except by chance.
National treasuries have the power to transfer resources from one generation to another. By buying and selling assets in the private markets, government creates opportunities for those of us alive today to transfer resources to or from those who are yet to be born. If government issues less debt than the value of public capital, there will be an implicit transfer from current to future generations. If it owns more debt, the implicit transfer is in the other direction.
The optimal value of debt, relative to public capital, is a political decision … Whatever principle the government does choose to fund its expenditure, the optimal value of public sector borrowing will not be zero, except by chance.
Today there seems to be a rather widespread consensus of public debt being acceptable as long as it doesn't increase too much and too fast. If the public debt-GDP ratio becomes higher than X % the likelihood of debt crisis and/or lower growth increases.
But in discussing within which margins public debt is feasible, the focus, however, is solely on the upper limit of indebtedness, and very few asks the question if maybe there is also a problem if public debt becomes too low.
The government's ability to conduct an "optimal" public debt policy may be negatively affected if public debt becomes too small. To guarantee a well-functioning secondary market in bonds it is essential that the government has access to a functioning market. If turnover and liquidity in the secondary market becomes too small, increased volatility and uncertainty will in the long run lead to an increase in borrowing costs. Ultimately there's even a risk that market makers would disappear, leaving bond market trading to be operated solely through brokered deals. As a kind of precautionary measure against this eventuality it may be argued – especially in times of financial turmoil and crises — that it is necessary to increase government borrowing and debt to ensure – in a longer run – good borrowing preparedness and a sustained (government) bond market.
The question if public debt is good and that we may actually have to little of it is one of our time's biggest questions. Giving the wrong answer to it will be costly.
One of the most effective ways of clearing up this most serious of all semantic confusions is to point out that private debt differs from national debt in being external. It is owed by one person to others. That is what makes it burdensome. Because it is interpersonal the proper analogy is not to national debt but to international debt…. But this does not hold for national debt which is owed by the nation to citizens of the same nation. There is no external creditor. We owe it to ourselves.
A variant of the false analogy is the declaration that national debt puts an unfair burden on our children, who are thereby made to pay for our extravagances. Very few economists need to be reminded that if our children or grandchildren repay some of the national debt these payments will be made to our children or grandchildren and to nobody else. Taking them altogether they will no more be impoverished by making the repayments than they will be enriched by receiving them.
Abba Lerner The Burden of the National Debt (1948)
Slim by chocolate — a severe case of goofed p-hacking21 November, 2016 at 17:12 | Posted in Statistics & Econometrics | 2 Comments
Frank randomly assigned the subjects to one of three diet groups. One group followed a low-carbohydrate diet. Another followed the same low-carb diet plus a daily 1.5 oz. bar of dark chocolate. And the rest, a control group, were instructed to make no changes to their current diet. They weighed themselves each morning for 21 days, and the study finished with a final round of questionnaires and blood tests …
Both of the treatment groups lost about 5 pounds over the course of the study, while the control group's average body weight fluctuated up and down around zero. But the people on the low-carb diet plus chocolate? They lost weight 10 percent faster. Not only was that difference statistically significant, but the chocolate group had better cholesterol readings and higher scores on the well-being survey.
I know what you're thinking. The study did show accelerated weight loss in the chocolate group—shouldn't we trust it? Isn't that how science works?
Here's a dirty little science secret: If you measure a large number of things about a small number of people, you are almost guaranteed to get a "statistically significant" result. Our study included 18 different measurements—weight, cholesterol, sodium, blood protein levels, sleep quality, well-being, etc.—from 15 people. (One subject was dropped.) That study design is a recipe for false positives.
Think of the measurements as lottery tickets. Each one has a small chance of paying off in the form of a "significant" result that we can spin a story around and sell to the media. The more tickets you buy, the more likely you are to win. We didn't know exactly what would pan out—the headline could have been that chocolate improves sleep or lowers blood pressure—but we knew our chances of getting at least one "statistically significant" result were pretty good.
Whenever you hear that phrase, it means that some result has a small p value. The letter p seems to have totemic power, but it's just a way to gauge the signal-to-noise ratio in the data. The conventional cutoff for being "significant" is 0.05, which means that there is just a 5 percent chance that your result is a random fluctuation. The more lottery tickets, the better your chances of getting a false positive. So how many tickets do you need to buy?
P(winning) = 1 – (1 – p)^n
With our 18 measurements, we had a 60% chance of getting some"significant" result with p < 0.05. (The measurements weren't independent, so it could be even higher.) The game was stacked in our favor.
It's called p-hacking—fiddling with your experimental design and data to push p under 0.05—and it's a big problem. Most scientists are honest and do it unconsciously. They get negative results, convince themselves they goofed, and repeat the experiment until it "works." Or they drop "outlier" data points.
Statistical inferences depend on both what actually happens and what might have happened. And Bohannon's (in)famous chocolate con more than anything else underscores the dangers of confusing the model with reality. Or as W.V.O. Quine had it:"Confusion of sign and object is the original sin."
There are no such things as free-standing probabilities – simply because probabilities are strictly seen only defined relative to chance set-ups – probabilistic nomological machines like flipping coins or roulette-wheels. And even these machines can be tricky to handle. Although prob(fair coin lands heads|I toss it) = prob(fair coin lands head & I toss it)|prob(fair coin lands heads) may be well-defined, it's not certain we can use it, since we cannot define the probability that I will toss the coin given the fact that I am not a nomological machine producing coin tosses.
No nomological machine – no probability.
Econometrics — science built on beliefs and untestable assumptions21 November, 2016 at 12:39 | Posted in Statistics & Econometrics | 1 Comment
What is distinctive about structural models, in contrast to forecasting models, is that they are supposed to be – when successfully supported by observation – informative about the impact of interventions in the economy. As such, they carry causal content about the structure of the economy. Therefore, structural models do not model mere functional relations supported by correlations, their functional relations have causal content which support counterfactuals about what would happen under certain changes or interventions.
This suggests an important question: just what is the causal content attributed to structural models in econometrics? And, from the more restricted perspective of this paper, what does this imply with respect to the interpretation of the error term? What does the error term represent causally in structural equation models in econometrics? And finally, what constraints are imposed on the error term for successful causal inference? …
I now consider briefly a key constraint that may be necessary for the error term to meet for using the model for causal inference. To keep the discussion simple, I look only at the simplest model
y= αx+u
The obvious experiment that comes to mind is to vary x, to see by how much y changes as a result. This sounds straight forward, one changes x, y changes and one calculates α as follows.
α = ∆y/ ∆x
Everything seems straightforward. However there is a concern since u is unobservable: how does one know that u has not also changed in changing x? Suppose that u does change so that there is hidden in the change in y a change in u, that is, the change in y is incorrectly measured by
∆yfalse= ∆y + ∆u
And thus that α is falsely measured as
αfalse =∆yfalse/∆x = ∆y/ ∆x +∆u/ ∆x = α + ∆u/ ∆x
Therefore, in order for the experiment to give the correct measurement for α, one needs either to know that u has not also changed or know by how much it has changed (if it has.) Since u is unobservable it is not known by how much u has changed. This leaves as the only option the need to know that in changing x, u has not also been unwittingly changed. Intuitively, this requires that it is known that whatever cause(s) of x which are used to change x, not also be causes of any of the factors hidden in u …
More generally, the example above shows a need to constrain the error term in the equation in a non-simultaneous structural equation model as follows. It requires that each right hand variable have a cause that causes y but not via any factor hidden in the error term. This imposes a limit on the common causes the factors in the error term can have with those factors explicitly modelled …
Consider briefly the testability of the two key assumptions brought to light in this section: (i) that the error term denotes the net impact of a set of omitted causal factors and (ii) that the each error term have at least one cause which does not cause the error term. Given these assumptions directly involve the factors omitted in the error term, testing these empirically seems impossible without information about what is hidden in the error term. This places the modeller in a difficult situation, how to know that something important has not been hidden. In practice, there will always be element of faith in the assumptions about the error term, assuming that assumptions like (i) and (ii) have been met, even if it is impossible to test these conclusively.
In econometrics textbooks it is often said that the error term in the regression models used represents the effect of the variables that were omitted from the model. The error term is somehow thought to be a 'cover-all' term representing omitted content in the model and necessary to include to 'save' the assumed deterministic relation between the other random variables included in the model. Error terms are usually assumed to be orthogonal (uncorrelated) to the explanatory variables. But since they are unobservable, they are also impossible to empirically test. And without justification of the orthogonality assumption, there is as a rule nothing to ensure identifiability:
With enough math, an author can be confident that most readers will never figure out where a FWUTV (facts with unknown truth value) is buried. A discussant or referee cannot say that an identification assumption is not credible if they cannot figure out what it is and are too embarrassed to ask.
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.
Follies and fallacies of Chicago economics20 November, 2016 at 13:28 | Posted in Economics | 2 Comments
Every dollar of increased government spending must correspond to one less dollar of private spending. Jobs created by stimulus spending are offset by jobs lost from the decline in private spending. We can build roads instead of factories, but fiscal stimulus can't help us to build more of both. This form of "crowding out" is just accounting, and doesn't rest on any perceptions or behavioral assumptions.
And the tiny little problem? It's utterly and completely wrong!
What Cochrane is reiterating here is nothing but Say's law, basically saying that savings are equal to investments, and that if the state increases investments, then private investments have to come down ('crowding out'). As an accounting identity there is of course nothing to say about the law, but as such it is also totally uninteresting from an economic point of view. As some of my Swedish forerunners — Gunnar Myrdal and Erik Lindahl — stressed more than 80 years ago, it's really a question of ex ante and ex post adjustments. And as further stressed by a famous English economist about the same time, what happens when ex ante savings and investments differ, is that we basically get output adjustments. GDP changes and so makes saving and investments equal ex ost. And this, nota bene, says nothing at all about the success or failure of fiscal policies!
Government borrowing is supposed to "crowd out" private investment.
The current reality is that on the contrary, the expenditure of the borrowed funds (unlike the expenditure of tax revenues) will generate added disposable income, enhance the demand for the products of private industry, and make private investment more profitable. As long as there are plenty of idle resources lying around, and monetary authorities behave sensibly, (instead of trying to counter the supposedly inflationary effect of the deficit) those with a prospect for profitable investment can be enabled to obtain financing. Under these circumstances, each additional dollar of deficit will in the medium long run induce two or more additional dollars of private investment. The capital created is an increment to someone's wealth and ipso facto someone's saving. "Supply creates its own demand" fails as soon as some of the income generated by the supply is saved, but investment does create its own saving, and more. Any crowding out that may occur is the result, not of underlying economic reality, but of inappropriate restrictive reactions on the part of a monetary authority in response to the deficit.
William Vickrey Fifteen Fatal Fallacies of Financial Fundamentalism
In a lecture on the US recession, Robert Lucas gave an outline of what the new classical school of macroeconomics today thinks on the latest downturns in the US economy and its future prospects.
Lucas starts by showing that real US GDP has grown at an average yearly rate of 3 per cent since 1870, with one big dip during the Depression of the 1930s and a big – but smaller – dip in the recent recession.
After stating his view that the US recession that started in 2008 was basically caused by a run for liquidity, Lucas then goes on to discuss the prospect of recovery from where the US economy is today, maintaining that past experience would suggest an "automatic" recovery, if the free market system is left to repair itself to equilibrium unimpeded by social welfare activities of the government.
As could be expected there is no room for any Keynesian type considerations on eventual shortages of aggregate demand discouraging the recovery of the economy. No, as usual in the new classical macroeconomic school's explanations and prescriptions, the blame game points to the government and its lack of supply side policies.
Lucas is convinced that what might arrest the recovery are higher taxes on the rich, greater government involvement in the medical sector and tougher regulations of the financial sector. But – if left to run its course unimpeded by European type welfare state activities -the free market will fix it all.
In a rather cavalier manner – without a hint of argument or presentation of empirical facts – Lucas dismisses even the possibility of a shortfall of demand. For someone who already 30 years ago proclaimed Keynesianism dead – "people don't take Keynesian theorizing seriously anymore; the audience starts to whisper and giggle to one another" – this is of course only what could be expected. Demand considerations are simply ruled out on whimsical theoretical-ideological grounds, much like we have seen other neo-liberal economists do over and over again in their attempts to explain away the fact that the latest economic crises shows how the markets have failed to deliver. If there is a problem with the economy, the true cause has to be government.
Chicago economics is a dangerous pseudo-scientific zombie ideology that ultimately relies on the poor having to pay for the mistakes of the rich. Trying to explain business cycles in terms of rational expectations has failed blatantly. Maybe it would be asking to much of freshwater economists like Lucas and Cochrane to concede that, but it's still a fact that ought to be embarrassing. My rational expectation is that 30 years from now, no one will know who Robert Lucas or John Cochrane was. John Maynard Keynes, on the other hand, will still be known as one of the masters of economics.
Top 10 critiques of econometrics20 November, 2016 at 11:01 | Posted in Statistics & Econometrics | Comments Off on Top 10 critiques of econometrics
•Achen, Christopher (1982). Interpreting and using regression. SAGE
•Berk, Richard (2004). Regression Analysis: A Constructive Critique. SAGE
•Freedman, David (1991). 'Statistical Models and Shoe Leather'. Sociological Methodology
•Kennedy, Peter (2002). 'Sinning in the Basement: What are the Rules? The Ten Commandments of Applied Econometrics'. Journal of Economic Surveys
•Keynes, John Maynard (1939). 'Professor Tinbergen's method'. Economic Journal
•Klees, Steven (2016). 'Inferences from regression analysis: are they valid?' Real-World Economics Review
•Lawson, Tony (1989). 'Realism and instrumentalism in the development of econometrics'. Oxford Economic Papers
•Leamer, Edward (1983). 'Let's take the con out of econometrics'. American Economic Review
•Lieberson, Stanley (1987). Making it count: the improvement of social research and theory. University of California Press
•Zaman, Asad (2012). 'Methodological Mistakes and Econometric Consequences'. International Econometric Review
Is Dani Rodrik really a pluralist?20 November, 2016 at 10:59 | Posted in Economics | Comments Off on Is Dani Rodrik really a pluralist?
Unlearning economics has a well-written and interesting review of Dani Rodrik's book Economics Rules up on Pieria. Although the reviewer thinks there is much in the book to like and appreciate, there are also things he strongly objects to. Such as Rodrik's stance on the issue of pluralism:
If Rodrik is at his strongest when discussing particular neoclassical models and their applications, he's at his weakest when discussing non-neoclassical and non-economic approaches. Just as his discussion of the former benefits from a broad array of concrete examples, his discussion of the latter suffers from a failure to discuss any examples, coupled with a series of sweeping, unsubstantiated assertions about what is wrong with them. Thus, despite the fact that Rodrik considers himself "well exposed to…different traditions within the social sciences", one is forced to conclude that he is almost completely ignorant of what they – as well as non-neoclassical economics – have to offer.
For example, Rodrik dismisses calls for methodological pluralism in economics with the bizarre claim that "no academic discipline is permissive of approaches that diverge too much from prevailing practices". This is patently untrue: pluralism is simply par for the course in every other social science and almost every other discipline. As Alan Freeman once commented, economics is actually "more committed to the unity of its doctrines than theology, whose benchmark simply states that 'Much of the excitement of the discipline lies in its contested nature.'" There is even a case that hard sciences like physics are more pluralist than economics. The insistence on a particular, rigid, deductive mathematical framework for approaching issues is a characteristic of economics and economics alone.
Most strangely, the case for economic pluralism follows directly from the defining theme of Rodrik's book: that economics is about choosing the best model for the best situation. If this is so, how can he dismiss non-mainstream models outright? This would entail either a convincing methodological demonstration of why these models can't be used in general, else a series of case studies of why popular non-neoclassical models are inferior to neoclassical ones. Without engaging with pluralist literature – model-based or otherwise – Rodrik has no clear reasons as to why he can reject such approaches. Personally, I would love to see some engagement from mainstream economists with ideas like Minsky's Financial Instability Hypothesis; capacity utilisation models; agent based models; cost-plus pricing; endogenous money. Note here that 'engagement' does not mean building one model which superficially claims to incorporate these ideas, then carrying on as normal.
Similar weakness is evident in Rodrik's discussion of other social sciences and his (largely unfavourable) comparison of them to economics. He makes another bizarre claim: that it would be "truly rare" for junior researchers to challenge more senior researchers in other disciplines, with his only citation as the Sokal affair, where a nonsense paper was accepted into a sociology journal. It is not clear how this is directly relevant, but presumably Rodrik's argument is that the editors simply accepted the paper because it referenced and agreed with them. But similar controversies have happened in many areas such as physics and electrical engineering. Are these just woolly, "interpretative" disciplines too? This really says more about the modern peer-review process than it does about any particular discipline. Besides, research suggests that economics is one of the most hierarchical disciplines in the social sciences.
Yours truly can't but agree. For my own take on Rodrik's book, see my review in Real-World Economics Review.
Econometric inferences — idiosyncratic, unstable and inconsistent19 November, 2016 at 11:49 | Posted in Statistics & Econometrics | 1 Comment
The impossibility of proper specification is true generally in regression analyses across the social sciences, whether we are looking at the factors affecting occupational status, voting behavior, etc. The problem is that as implied by the three conditions for regression analyses to yield accurate, unbiased estimates, you need to investigate a phenomenon that has underlying mathematical regularities – and, moreover, you need to know what they are. Neither seems true. I have no reason to believe that the way in which multiple factors affect earnings, student achievement, and GNP have some underlying mathematical regularity across individuals or countries. More likely, each individual or country has a different function, and one that changes over time. Even if there was some constancy, the processes are so complex that we have no idea of what the function looks like.
Researchers recognize that they do not know the true function and seem to treat, usually implicitly, their results as a good-enough approximation. But there is no basis for the belief that the results of what is run in practice is anything close to the underlying phenomenon, even if there is an underlying phenomenon. This just seems to be wishful thinking. Most regression analysis research doesn't even pay lip service to theoretical regularities. But you can't just regress anything you want and expect the results to approximate reality. And even when researchers take somewhat seriously the need to have an underlying theoretical framework – as they have, at least to some extent, in the examples of studies of earnings, educational achievement, and GNP that I have used to illustrate my argument – they are so far from the conditions necessary for proper specification that one can have no confidence in the validity of the results.
Most work in econometrics and regression analysis is made on the assumption that the researcher has a theoretical model that is 'true.' Based on this belief of having a correct specification for an econometric model or running a regression, one proceeds as if the only problem remaining to solve have to do with measurement and observation.
The problem is that there is pretty little to support the perfect specification assumption. Looking around in social science and economics we don't find a single regression or econometric model that lives up to the standards set by the 'true' theoretical model — and there is nothing that gives us reason to believe things will be different in the future.
To think that we are being able to construct a model where all relevant variables are included and correctly specify the functional relationships that exist between them, is not only a belief with little support, but a belief impossible to support.
The theories we work with when building our econometric regression models are insufficient. No matter what we study, there are always some variables missing, and we don't know the correct way to functionally specify the relationships between the variables.
Every regression model constructed is misspecified. There are always an endless list of possible variables to include, and endless possible ways to specify the relationships between them. So every applied econometrician comes up with his own specification and 'parameter' estimates. The econometric Holy Grail of consistent and stable parameter-values is nothing but a dream.
The theoretical conditions that have to be fulfilled for regression analysis and econometrics to really work are nowhere even closely met in reality. Making outlandish statistical assumptions does not provide a solid ground for doing relevant social science and economics. Although regression analysis and econometrics have become the most used quantitative methods in social sciences and economics today, it's still a fact that the inferences made from them are invalid.
Friedman's methodological folly19 November, 2016 at 10:59 | Posted in Theory of Science & Methodology | 2 Comments
Friedman enters this scene arguing that all we need to do is predict successfully, that this can be done even without realistic theories, and that unrealistic theories are to be preferred to realistic ones, essentially because they can usually be more parsimonious.
The first thing to note about this response is that Friedman is attempting to turn inevitable failure into a virtue. In the context of economic modelling, the need to produce formulations in terms of systems of isolated atoms, where these are not characteristic of social reality, means that unrealistic formulations are more or less unavoidable. Arguing that they are to be preferred to realistic ones in this context belies the fact that there is not a choice …
My own response to Friedman's intervention is that it was mostly an irrelevancy, but one that has been opportunistically grasped by some as a supposed defence of the profusion of unrealistic assumptions in economics. This would work if successful prediction were possible. But usually it is not.
If scientific progress in economics – as Robert Lucas and other latter days followers of Milton Friedman seem to think – lies in our ability to tell 'better and better stories' one would of course expect economics journals being filled with articles supporting the stories with empirical evidence confirming the predictions. However, the journals still show a striking and embarrassing paucity of empirical studies that (try to) substantiate these predictive claims. Equally amazing is how little one has to say about the relationship between the model and real world target systems. It is as though explicit discussion, argumentation and justification on the subject isn't considered to be required.
If the ultimate criterion of success of a model is to what extent it predicts and coheres with (parts of) reality, modern mainstream economics seems to be a hopeless misallocation of scientific resources. To focus scientific endeavours on proving things in models, is a gross misapprehension of what an economic theory ought to be about. Deductivist models and methods disconnected from reality are not relevant to predict, explain or understand real-world economies.
There can be no theory without assumptions since it is the assumptions embodied in a theory that provide, by way of reason and logic, the implications by which the subject matter of a scientific discipline can be understood and explained. These same assumptions provide, again, by way of reason and logic, the predictions that can be compared with empirical evidence to test the validity of a theory. It is a theory's assumptions that are the premises in the logical arguments that give a theory's explanations meaning, and to the extent those assumptions are false, the explanations the theory provides are meaningless no matter how logically powerful or mathematically sophisticated those explanations based on false assumptions may seem to be.
Happy meal19 November, 2016 at 10:32 | Posted in Varia | Comments Off on Happy meal
(h/t Jeanette Meyer)
Economics — an academic discipline gone badly wrong18 November, 2016 at 18:30 | Posted in Economics | 2 Comments
Three economics graduates have savaged the way the subject is taught at many universities.
Joe Earle, Cahal Moran and Zach Ward-Perkins are all, they write in The Econocracy: The Perils of Leaving Economics to the Experts, "of the generation that came of age in the maelstrom of the 2008 global financial crisis" and embarked on economics degrees at the University of Manchester in 2011 precisely in order to better "understand and shape the world".
By their second year, they were sufficiently disillusioned to start "a campaign to reform economics education", and they became active members of the Rethinking Economics network, which currently has over 40 groups in 13 countries. Their book sets out to examine how far their own experience is symptomatic of a more general malaise.
In order to do so, the authors carried out, they believe for the first time, "an in-depth curriculum review of seven [elite British] Russell Group universities…the course guides and examinations", making a total of 174 economics modules in all.
What emerged was "a remarkable similarity in the content and structure of economics courses", where "students only learn one particular type of [neoclassical] economics" and "are taught to accept this type of economics in an uncritical manner".
In developing these arguments, the authors point to "the central role textbooks play" in economics degrees, even though in other social sciences students are expected to "engag[e] critically with the discipline's academic literature", in order to gain "an understanding of the debates and research agendas" and to "engage independently and critically with academics while improving research and review skills".
Economics, in contrast, is treated as "a closed, fixed body of knowledge…It is as if students must be initiated into neoclassical economics in a way that shields them entirely from the real world, potential criticism or alternatives."
NHST — a case of statistical pseudoscience18 November, 2016 at 18:13 | Posted in Statistics & Econometrics | Comments Off on NHST — a case of statistical pseudoscience
NHST is an incompatible amalgamation of the theories of Fisher and of Neyman and Pearson (Gigerenzer, 2004). Curiously, it is an amalgamation that is technically reassuring despite it being, philosophically, pseudoscience. More interestingly, the numerous critiques raised against it for the past 80 years have not only failed to debunk NHST from the researcher's statistical toolbox, they have also failed to be widely known, to find their way into statistics manuals, to be edited out of journal submission requirements, and to be flagged up by peer-reviewers (e.g., Gigerenzer, 2004). NHST effectively negates the benefits that could be gained from Fisher's and from Neyman-Pearson's theories; it also slows scientific progress (Savage, 1957; Carver, 1978, 1993) and may be fostering pseudoscience. The best option would be to ditch NHST altogether and revert to the theories of Fisher and of Neyman-Pearson as—and when—appropriate. For everything else, there are alternative tools, among them exploratory data analysis (Tukey, 1977), effect sizes (Cohen, 1988), confidence intervals (Neyman, 1935), meta-analysis (Rosenthal, 1984), Bayesian applications (Dienes, 2014) and, chiefly, honest critical thinking (Fisher, 1960).
Making It Count17 November, 2016 at 23:00 | Posted in Statistics & Econometrics | 2 Comments
Modern econometrics is fundamentally based on assuming — usually without any explicit justification — that we can gain causal knowledge by considering independent variables that may have an impact on the variation of a dependent variable. This is however, far from self-evident. Often the fundamental causes are constant forces that are not amenable to the kind of analysis econometrics supplies us with. As Stanley Lieberson has it in his modern classic Making It Count:
One can always say whether, in a given empirical context, a given variable or theory accounts for more variation than another. But it is almost certain that the variation observed is not universal over time and place. Hence the use of such a criterion first requires a conclusion about the variation over time and place in the dependent variable. If such an analysis is not forthcoming, the theoretical conclusion is undermined by the absence of information …
Moreover, it is questionable whether one can draw much of a conclusion about causal forces from simple analysis of the observed variation … To wit, it is vital that one have an understanding, or at least a working hypothesis, about what is causing the event per se; variation in the magnitude of the event will not provide the answer to that question.
Trygve Haavelmo was making a somewhat similar point back in 1941, when criticizing the treatmeant of the interest variable in Tinbergen's regression analyses. The regression coefficient of the interest rate variable being zero was according to Haavelmo not sufficient for inferring that "variations in the rate of interest play only a minor role, or no role at all, in the changes in investment activity." Interest rates may very well play a decisive indirect role by influencing other causally effective variables. And:
the rate of interest may not have varied much during the statistical testing period, and for this reason the rate of interest would not "explain" very much of the variation in net profit (and thereby the variation in investment) which has actually taken place during this period. But one cannot conclude that the rate of influence would be inefficient as an autonomous regulator, which is, after all, the important point.
Causality in economics — and other social sciences — can never solely be a question of statistical inference. Causality entails more than predictability, and to really in depth explain social phenomena requires theory. Analysis of variation — the foundation of all econometrics — can never in itself reveal how these variations are brought about. First when we are able to tie actions, processes or structures to the statistical relations detected, can we say that we are getting at relevant explanations of causation. Too much in love with axiomatic-deductive modeling, neoclassical economists especially tend to forget that accounting for causation — how causes bring about their effects — demands deep subject-matter knowledge and acquaintance with the intricate fabrics and contexts. As already Keynes argued in his A Treatise on Probability, statistics and econometrics should not primarily be seen as means of inferring causality from observational data, but rather as description of patterns of associations and correlations that we may use as suggestions of possible causal realations.
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Näytä Data
18/10/2018 12:24
on CO2 emissions in South, East, and Southeast Asia in 1980 and 2012. Unfortunately 2012 is the most current year for this gridded dataset. Nevertheless, these maps definately tell a story:
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Madagascar, Southeast Africa Monkey
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My friend in Southeast that have been denying the fact that 2NB project is work in progress in the east. Shame on U all. Stop the denial and give PMB so me accolades. @APCUKingdom @ogundamisi @Peter__Obi
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People from southeast texas will put zummos in any meal that you can add meat to.
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Them 's coming in just let it stack up , Don't ride my waves dawg Back up! ain't NEVER had shit I gotta act up
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Nichrome has a higher resistance than copper
To investigate the resistance of the 3 different types of wires, constantan, copper and nichrome, by using 1 set of circuit research: constantan a metal alloy made of 45 percent nickel and 55 percent copper. Nichrome wire prices higher prices 2 nichrome has less resistance in a similar comparison, kanthal wire is found to have lower resistance than nichrome. Cirris systems provides you with a calculator to measure wire resistance learn more about wire resistance calculations here, and visit cirris systems today.
Chapter 27 current and resistance solutions of home work problems 271 problem 2714 (in the text book) a resistor is constructed of a carbon rod that has a uniform cross-sectional area of 500 mm2. Nichrome wire has relatively higher resistance levels to prevent the material beneath the wire from oxidizing, which prevents the wire inside from burning or breaking out, according to hm wire international also referred to as alloy 650, nichrome wire is a resistance alloy that is mostly used for. If you only have one long nichrome wire it can be looped back and forth as shown in the first illustration if there is an odd number of passes, use a copper wire to connect the end of the nichrome to the opposite end of the. Resistance wire is a high-resistance electrical wire that is often found in industrial applications including electrical ovens, toasters, and heating elements the two major types of resistance wire are kanthal and nichrome wire kanthal is a ferritic iron-chromium-aluminum alloy that can be in high.
Nichrome wire to be tested this means that longer wires have higher resistance value is much higher than copper it is not nearly as good a conductor as copper. The thickness of nichrome wire and its effect on its resistance - the thickness of nichrome wire and its effect on its resistance introduction a current is the flow of negative electrons around a circuit. The resistance of an object depends on its shape and the material of which it is composed the cylindrical resistor in figure 1 is easy to analyze, and, by so doing, we can gain insight into the resistance of more complicated shapes. Electrical resistivity thick wire has a higher resistance then with a shorter and thinner wire resistance of copper wire, resistance of nichrome wire,. Electrical resistance, denoted by r, is the total opposition to the passage of electric current, or the flow of electrons resistance depends on the material, area, and length of what the current is passing through, so the number varies depending on where resistance is measured.
Resistivity is a measure of how strongly a material opposes the flow of electric current good electrical conductors have very low resistivities and good insulators have very high resistivities resistivity is denoted by the greek symbol rho (ρ) and can be determined by rearranging this formula: r. Resistance wire is a high-resistance electrical wire that is often found in industrial applications including electrical ovens, toasters, and heating elements. A 12-gauge wire is wider than 14-gauge wire and thus has less resistance the lesser resistance of 12-gauge wire means that it can allow charge to flow through it at a greater rate - that is, allow a larger current. The length and the material that the conductor is made from different wire sizes have different ohm/foot the longer the length of the conductor the higher.
Wire - information copper & resistance wires 'karma' is another resistance wire alloy that has a higher resistance per metre than nichrome. Material and shape dependence of resistance the resistance of an object depends on its shape and the material of which it is composed the cylindrical resistor in is easy to analyze, and, by so doing, we can gain insight into the resistance. I think what you mean is: if you put the same voltage on a wire of each material, which one let's more current through the voltage is the push on the electrons, and their flow is the current if you have a nichrome wire and a copper wire the same size and shape, with the same voltage on them.
Improved anode-circuit parasitic-suppression for modern leakage current will be much higher than the has about 60 times more resistance than does copper or. Resistance outline l earning outcomes after completing this chapter you will be able to • explain the nature of resistance • describe the relationship between resistance. P23 p24 lesson 4 resistance nichrome wire has a higher resistance than a copper wire of a steeper gradient and so has a lower resistance than the nichrome. Electrical resistance, copper and steel wires release less heat to be absorbed by the smoke fluids smaller diameter of nichrome wire has higher resistance,.
Resistance versus temperature in a nichrome wire sarah lemieux, ann kuan, jennie shen, alyssa fuji ib physics ii murray 1/13/14 return to research contents. Nichrome/tophet "a" is a nickel-chromium resistance alloy for operating temperatures up to 2150° f it is non-magnetic, highly corrosion resistant and has a lower coefficient of resistance than other heater element alloys. Say i have a 12v car battery with 001 ohms of internal resistance (if im wrong about that, just ignore it), meaning a dead short with copper. Cleveland wire cloth has woven nearly every ductile metal or alloy that can be drawn into wire we specialize in corrosion-resistant and heat-resistant alloys woven to the exact specifications of your project.


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Jackie robinson was that player and jackie robinson changed the game, america, and history by looking specifically at his childhood adversity, college life and the hardships he encountered by becoming the first black player in the game, it will be shown why jackie robinson is a great american story and hero. Essay about jackie robinson roosevelt robinson was born on january 31, 1919, in cairo, georgia he was the youngest of five children jackie grew up very poor, but little did he know that his athletic ability would open the doors for his future. Jackie roosevelt robinson was born on january 31st 1919 essay about jackie robinson teens that struggled through life like him jackie robinson came from a .
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Free essay on jackie robinson biography, book review, essay available totally free at echeatcom, the largest free essay community. Jackie robinson the author of jackie robinson is manfred widower the book was published by athenian books in 1993 the book "jackie robinson" is a biography because it talks about his life and how he grew up as a black baseball player for the dodgers. Jackie robinson jackie robinson's full name is jack roosevelt robinson he was born on january 31, 1919 in cairo, georgia jackie robinson was not a slave but his grandfather was born into slavery his parents, mallie and jerry robinson lived during the time slavery was ended jackie moved to . Jackie robinson essay examples a biography of jack roosevelt robinson a baseball player 1,219 words 3 pages the life and career of jackie robinson, the first .
View essay - jackie robinson from english an 5057 at english modern school the breaking of the color barrier by jillian favor i ii introduction body aearly life outline 1family 1mother and. The main characters of the story are jackie robinson and mallie robinson jackie is black 56 and was very athletic and diligent jackie robinson had a harsh life . Edwin arlington robinsons the mill essay jackie robinson essay roosevelt robinson was born on january 31, 1919, in cairo, georgia is what jackie's early . The biography of jackie robinson essay a pages: jackie roosevelt robinson is famously known for breaking the color barrier in baseball when he joined baseball to . Biography: where did jackie robinson grow up jack roosevelt robinson was born on january 31, 1919 in cairo, georgiahe was the youngest of five children.
Essays jackie robinson report to perpetuate the memory of jackie robinson and his achievements jackie robinson was inducted into ucla's hall of fame in 1984 . Jackie roosevelt robinson began to break color barriers from the beginning of his heroic life in 1920, his mother mallie robinson packed up jackie and his four siblings and moved to pasadena, california in search of a safer environment due to the fact that georgia had become quite violent towards blacks with the emergence of the klu klux klan . Jackie robinson biography essays: over 180,000 jackie robinson biography essays, jackie robinson biography term papers, jackie robinson biography research paper, book reports 184 990 essays, term and research papers available for unlimited access.
Jackie robinson influenced the modern day black society by breaking the laws of segregation and becoming one of america's greatest baseball heroes jack roosevelt robinson was a fighter from the day he was born on january 31, 1919. Jackie robinson: baseball great, authored by nathan huggins, features this gifted individual known as jack ("jackie") roosevelt robinson throughout the biography, huggins uses a variety a rhetorical terms to depict the lifestyle and numerous hardships robinson faced throughout his life trek of becoming the first african-american player in . "a life is not important except in the impact it has on other lives" -jack roosevelt "jackie" robinson(biographycom) on april 15,1947 jackie roosevelt robinson broke the color barrier when he stepped out on to ebbets field everyone didn't think he could last long jackie was born in cairo . Jackie robinsons favorite quote was a life is not important except in the impact it has on other lives /people/jackie-robinson-9460813 jackie roosevelt robinson .
Jackie robinson essay, jackie robinson term paper help he warned jackie of the problems he would face as the first black player in a white team his life and . Jackie robinson was the first african-american to play in the major league baseball (mlb) read about his childhood, achievements, life and timeline in this biography. Research papers on jackie robinson davis' pride in robinson's achievements underscores a larger issue that jack roosevelt "jackie" robinson was born .


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