## On causality and econometrics

20 Jan, 2020 at 11:45 | Posted in Statistics & Econometrics | Leave a commentThe point is that a superficial analysis, which only looks at the numbers, without attempting to assess the underlying causal structures, cannot lead to a satisfactory data analysis … We must go out into the real world and look at the structural details of how events occur … The idea that the numbers by themselves can provide us with causal information is false. It is also false that a meaningful analysis of data can be done without taking any stand on the real-world causal mechanism … These issues are of extreme important with reference to Big Data and Machine Learning. Machines cannot expend shoe leather, and enormous amounts of data cannot provide us knowledge of the causal mechanisms in a mechanical way. However, a small amount of knowledge of real-world structures used as causal input can lead to substantial payoffs in terms of meaningful data analysis. The problem with current econometric techniques is that they do not have any scope for input of causal information – the language of econometrics does not have the vocabulary required to talk about causal concepts.

What Asad Zaman tells us in his splendid set of lectures is that causality in social sciences can never solely be a question of statistical inference. Causality entails more than predictability, and to really in depth explain social phenomena require 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.

Most facts have many different, possible, alternative explanations, but we want to find the best of all contrastive (since all real explanation takes place relative to a set of alternatives) explanations. So which is the best explanation? Many scientists, influenced by statistical reasoning, think that the likeliest explanation is the best explanation. But the likelihood of x is not in itself a strong argument for thinking it explains y. I would rather argue that what makes one explanation better than another are things like aiming for and finding powerful, deep, causal, features and mechanisms that we have warranted and justified reasons to believe in. Statistical — especially the variety based on a Bayesian epistemology — reasoning generally has no room for these kinds of explanatory considerations. The only thing that matters is the probabilistic relation between evidence and hypothesis. That is also one of the main reasons I find abduction — inference to the best explanation — a better description and account of what constitute actual scientific reasoning and inferences.

Some statisticians and data scientists think that algorithmic formalisms somehow give them access to causality. That is, however, simply not true. Assuming ‘convenient’ things like faithfulness or stability is not to give proofs. It’s to assume what has to be proven. Deductive-axiomatic methods used in statistics do no produce evidence for causal inferences. The real causality we are searching for is the one existing in the real world around us. If there is no warranted connection between axiomatically derived theorems and the real-world, well, then we haven’t really obtained the causation we are looking for.

## Experiments in social sciences

18 Jan, 2020 at 22:30 | Posted in Statistics & Econometrics | 1 CommentHow, then, can social scientists best make inferences about causal effects? One option is true experimentation … Random assignment ensures that any differences in outcomes between the groups are due either to chance error or to the causal effect … If the experiment were to be repeated over and over, the groups would not differ, on average, in the values of potential confounders. Thus, the average of the average difference of group outcomes, across these many experiments, would equal the true difference in outcomes … The key point is that randomization is powerful because it obviates confounding …

Thad Dunning’s book is a very useful guide for social scientists interested in research methodology in general and natural experiments in specific. Dunning argues that since random or as-if random assignment in natural experiments obviates the need for controlling potential confounders, this kind of “simple and transparent” design-based research method is preferable to more traditional multivariate regression analysis where the controlling only comes in* ex post* via statistical modelling.

But — there is always a but …

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

The problem with that simplistic view on randomization is that the claims made are exaggerated and sometimes even false.

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

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 into the picture.

The assumption of imaginary ‘super populations’ is one of the many dubious assumptions used in modern econometrics.

As social scientists — and economists — 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 a domain of probability theory and sample space of infinite populations 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 science with aspirations of explaining real-world socio-economic processes, structures or events. It’s not tenable. Why should we as social scientists — and not as pure mathematicians working with formal-axiomatic systems without the urge to confront our models with real target systems — unquestioningly accept models based on concepts like the ‘infinite super populations’ used in e.g. the ‘potential outcome’ framework that has become so popular lately in social sciences?

One could, of course, treat observational or experimental data as random samples from real populations. I have no problem with that (although it has to be noted that most ‘natural experiments’ are *not* based on random sampling from some underlying population — which, of course, means that the effect-estimators, strictly seen, only are unbiased for the specific groups studied). 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 ‘infinite super populations.’

In social sciences — including economics — it’s always wise to ponder C. S. Peirce’s remark that universes are not as common as peanuts …

## Dynamic and static interpretations of regression coefficients (wonkish)

15 Jan, 2020 at 19:33 | Posted in Statistics & Econometrics | Leave a commentWhen econometric and statistical textbooks present simple (and multiple) regression analysis for cross-sectional data, they often do it with regressions like “regress test score (y) on study hours (x)” and get the result

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

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

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

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

## Why all RCTs are biased

15 Jan, 2020 at 17:22 | Posted in Statistics & Econometrics | Leave a commentRandomised experiments require much more than just randomising an experiment to identify a treatment’s effectiveness. They involve many decisions and complex steps that bring their own assumptions and degree of bias before, during and after randomisation …

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

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

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

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

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

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

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

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

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

## How to teach econometrics

6 Jan, 2020 at 14:54 | Posted in Statistics & Econometrics | 3 CommentsProfessor Swann (2019) seems implicitly to be endorsing the traditional theorem/proof style for teaching econometrics but with a few more theorems to be memorized. This style of teaching prepares students to join the monks in Asymptopia, a small pristine mountain village, where the monks read the tomes, worship the god of Consistency, and pray all day for the coming of the Revelation, when the estimates with an infinite sample will be revealed. Dirty limited real data sets with unknown properties are not allowed in Asymptopia, only hypothetical data with known properties. Not far away in the mountains is the village of Euphoria where celibate priests compose essays regarding human sexuality. Down on the plains is the very large city of Real Data, where applied economists torture dirty data until the data confess, providing the right signs and big t-values. Although Real Data is infinitely far from Asymptopia, these applied econometricians are fond of supporting the “Scientific” character of their work with quotations from the spiritual essays of the Monks of Asymptopia.

## Markov’s inequality (wonkish)

20 Dec, 2019 at 10:13 | Posted in Statistics & Econometrics | 1 CommentOne of the most beautiful results of probability theory is **Markov’s inequality** (after the Russian mathematician Andrei Markov (1856-1922)):

**If X is a non-negative stochastic variable (X ≥ 0) with a finite expectation value E(X), then for every a > 0**

**P{X ≥ a} ≤ E(X)/a**

If the production of cars in a factory during a week is assumed to be a stochastic variable with an expectation value (mean) of 50 units, we can – based on nothing else but the inequality – conclude that the probability that the production for a week would be greater than 100 units can not exceed 50% [P(X≥100)≤(50/100)=0.5 = 50%]

I still feel humble awe at this immensely powerful result. Without knowing anything else but an expected value (mean) of a probability distribution we can deduce upper limits for probabilities. The result hits me as equally surprising today as forty years ago when I first run into it as a student of mathematical statistics.

[For a derivation of the inequality, see e.g. Sheldon Ross, *Introduction to Probability and Statistics for Engineers and Scientists*, Academic Press, 2009]

## Econometrics — fictions of limited value

19 Nov, 2019 at 14:36 | Posted in Statistics & Econometrics | 4 CommentsIt is often said that the error term in a regression equation represents the effect of the variables that were omitted from the equation. This is unsatisfactory …

There is no easy way out of the difficulty. The conventional interpretation for error terms needs to be reconsidered. At a minimum, something like this would need to be said:

The error term represents the combined effect of the omitted variables, assuming that

(i) the combined effect of the omitted variables is independent of each variable included in the equation,

(ii) the combined effect of the omitted variables is independent across subjects,

(iii) the combined effect of the omitted variables has expectation 0.This is distinctly harder to swallow.

Yes, indeed, that *is* harder to swallow.

Those conditions on the error term actually mean 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.

But that is actually impossible to fully manage in reality!

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 (usually just *assuming* linearity).

*Every* regression model constructed is misspecified. There is 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. No wonder that the econometric Holy Grail of consistent and stable parameter-values is still nothing but a dream.

In order to draw inferences from data as described by econometric texts, it is necessary to make whimsical assumptions. The professional audience consequently and properly withholds belief until an inference is shown to be adequately insensitive to the choice of assumptions. The haphazard way we individually and collectively study the fragility of inferences leaves most of us unconvinced that any inference is believable. If we are to make effective use of our scarce data resource, it is therefore important that we study fragility in a much more systematic way. If it turns out that almost all inferences from economic data are fragile, I suppose we shall have to revert to our old methods …

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.

Real-world social systems are not governed by stable causal mechanisms or capacities. As Keynes noticed when he first launched his attack against econometrics and inferential statistics already in the 1920s:

The atomic hypothesis which has worked so splendidly in Physics breaks down in Psychics. We are faced at every turn with the problems of Organic Unity, of Discreteness, of Discontinuity – the whole is not equal to the sum of the parts, comparisons of quantity fails us, small changes produce large effects, the assumptions of a uniform and homogeneous continuum are not satisfied. Thus the results of Mathematical Psychics turn out to be derivative, not fundamental, indexes, not measurements, first approximations at the best; and fallible indexes, dubious approximations at that, with much doubt added as to what, if anything, they are indexes or approximations of.

The kinds of laws and relations that econom(etr)ics has established, are laws and relations about entities in models that presuppose causal mechanisms being atomistic and additive. When causal mechanisms operate in real-world social target systems they only do it in ever-changing and unstable combinations where the whole is more than a mechanical sum of parts. If economic regularities obtain they do it (as a rule) only because we engineered them for that purpose. Outside man-made “nomological machines” they are rare, or even non-existent. Unfortunately, that also makes most of the achievements of econometrics – as most of the contemporary endeavours of economic theoretical modelling – rather useless.

Regression models are widely used by social scientists to make causal inferences; such models are now almost a routine way of demonstrating counterfactuals.

However, the “demonstrations” generally turn out to depend on a series of untested, even unarticulated, technical assumptions …Developing appropriate models is a serious problem in statistics; testing the connection to the phenomena is even more serious …In our days, serious arguments have been made from data. Beautiful, delicate theorems have been proved, although the connection with data analysis often remains to be established. And

an enormous amount of fiction has been produced, masquerading as rigorous science.

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.

## DAGs — colliders and d-separation (wonkish)

14 Nov, 2019 at 15:48 | Posted in Statistics & Econometrics | Comments Off on DAGs — colliders and d-separation (wonkish)

Great lecture on something that most students have problems with when introduced to causal graph theory.

## Why the father of modern statistics — R A Fisher — denied smoking causes cancer

10 Nov, 2019 at 15:16 | Posted in Statistics & Econometrics | 2 CommentsIn 1959, Fisher denounced his colleagues for manufacturing anti-smoking “propaganda” … He did not dispute that smoking and lung cancer tended to rise and fall together—that is, that they were correlated. But Hill and Doll and the entire British medical establishment had committed “an error … of an old kind, in arguing from correlation to causation,” he wrote in a letter to

Nature…Most researchers had evaluated the association between smoking and cancer and concluded that the former caused the latter. But what if the opposite were true?

For a time, many of Fisher’s peers in academic statistics, including Jerzy Neyman, questioned the validity of a causal claim. But before long, the majority buckled under the weight of mounting evidence and overwhelming consensus …

In his review of the debate, the epidemiologist Paul Stolley lambasts Fisher for being “unwilling to seriously examine the data and to review all the evidence before him to try to reach a judicious conclusion.” According to Stolley, Fisher undermined Hill and Doll’s conclusions by cherry picking irregular findings and blowing them out of proportion … Others have offered less charitable interpretations … even [suggesting] that his skepticism had been bought. The Tobacco Manufacturers’ Committee had agreed to fund Fisher’s research into possible genetic causes of both smoking and lung cancer. And though it seems unlikely that a man who routinely insulted his peers and jeopardized his career in order to prove that he was right would sell his professional opinion at such an old age, some still regard him as doing so.

If Fisher wasn’t swayed by money, it seems more likely that he was influenced by politics.

Throughout his life, Fisher was an unflinching reactionary. In 1911, while studying at Cambridge, he helped found the university’s Eugenics Society. Though many well-educated English men of the day embraced this ideology, Fisher took to the issue with an unusual fervency. Throughout his career, he intermittently wrote papers on the subject. A particular concern of Fisher’s was that upper class families were having fewer children than their poorer and less educated counter-parts. At one point, he suggested that the government pay “intelligent” couples to procreate … These political leanings may have colored his views on smoking.

## Collider attributes in graph theory (wonkish)

7 Nov, 2019 at 11:49 | Posted in Statistics & Econometrics | 3 CommentsWhy would two independent variables suddenly become dependent when we condition on their common effect? To answer this question, we return again to the definition of conditioning as filtering by the value of the conditioning variable. When we condition on Z, we limit our comparisons to cases in which Z takes the same value. But remember that Z depends, for its value, on X and Y. So, when comparing cases where Z takes some value, any change in value of X must be compensated for by a change in the value of Y — otherwise, the value of Z would change as well.

The reasoning behind this attribute of colliders — that conditioning on a collision node produces a dependence between the node’s parents — can be difficult to grasp at first. In the most basic situation where Z = X + Y, and X and Y are independent variables, we have the follow- ing logic: If I tell you that X = 3, you learn nothing about the potential value of Y, because the two numbers are independent. On the other hand, if I start by telling you that Z = 10, then telling you that X = 3 immediately tells you that Y must be 7. Thus, X and Y are dependent, given that Z = 10.

People usually find this collider attribute rather perplexing. Why? My guess is the reason is most people wrongly think there can be no correlation without causation.

## Why validating assumptions is so important in science

5 Nov, 2019 at 14:05 | Posted in Statistics & Econometrics | 3 CommentsAn ongoing concern is that excessive focus on formal modeling and statistics can lead to neglect of practical issues and to overconfidence in formal results … Analysis interpretation depends on contextual judgments about how reality is to be mapped onto the model, and how the formal analysis results are to be mapped back into reality. But overconfidence in formal outputs is only to be expected when much labor has gone into deductive reasoning. First, there is a need to feel the labor was justified, and one way to do so is to believe the formal deduction produced important conclusions. Second, there seems to be a pervasive human aversion to uncertainty, and one way to reduce feelings of uncertainty is to invest faith in deduction as a sufficient guide to truth. Unfortunately, such faith is as logically unjustified as any religious creed, since a deduction produces certainty about the real world only when its assumptions about the real world are certain …

Unfortunately, assumption uncertainty reduces the status of deductions and statistical computations to exercises in hypothetical reasoning – they provide best-case scenarios of what we could infer from specific data (which are assumed to have only specific, known problems). Even more unfortunate, however, is that this exercise is deceptive to the extent it ignores or misrepresents available information, and makes hidden assumptions that are unsupported by data …

Despite assumption uncertainties, modelers often express only the uncertainties derived within their modeling assumptions, sometimes to disastrous consequences. Econometrics supplies dramatic cautionary examples in which complex modeling has failed miserably in important applications …

Yes, indeed, econometrics fails miserably over and over again. One reason why it does, is that the error term in the regression models used are thought of as representing the effect of the variables that were omitted from the models. 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.

## Confusing statistics and research

3 Nov, 2019 at 10:58 | Posted in Statistics & Econometrics | Comments Off on Confusing statistics and researchCoupled with downright incompetence in statistics, we often find the syndrome that I have come to call

statisticism: the notion that computing is synonymous with doing research, the naïve faith that statistics is a complete or sufficient basis for scientific methodology, the superstition that statistical formulas exist for evaluating such things as the relative merits of different substantive theories or the “importance” of the causes of a “dependent variable”; and the delusion that decomposing the covariations of some arbitrary and haphazardly assembled collection of variables can somehow justify not only a “causal model” but also, praise a mark, a “measurement model.” There would be no point in deploring such caricatures of the scientific enterprise if there were a clearly identifiable sector of social science research wherein such fallacies were clearly recognized and emphatically out of bounds.

Wise words well worth pondering on.

As long as economists and statisticians cannot really identify their statistical theories with real-world phenomena there is no real warrant for taking their statistical inferences seriously.

Just as there is no such thing as a ‘free lunch,’ there is no such thing as a ‘free probability.’ To be able at all to talk about probabilities, you have to specify a model. If there is no chance set-up or model that generates the probabilistic outcomes or events – in statistics one refers to any process where you observe or measure as an experiment (rolling a die) and the results obtained as the outcomes of events (number of points rolled with the die, being e. g. 3 or 5) of the experiment – there strictly seen is no event at all.

Probability is a relational thing. It always must come with a specification of the model from which it is calculated. And then to be of any empirical scientific value it has to be shown to coincide with (or at least converge to or approximate) real data generating processes or structures — something seldom or never done!

And this is the basic problem with economic data. If you have a fair roulette-wheel, you can arguably specify probabilities and probability density distributions. But how do you conceive of the analogous ‘nomological machines’ for prices, gross domestic product, income distribution etc? Only by a leap of faith. And that does not suffice. You have to come up with some really good arguments if you want to persuade people into believing in the existence of socio-economic structures that generate data with characteristics conceivable as stochastic events portrayed by probabilistic density distributions!

## The pitfalls of econometrics

24 Oct, 2019 at 14:47 | Posted in Statistics & Econometrics | 3 CommentsEd Leamer’s Tantalus on the Road to Asymptopia is one of my favourite critiques of econometrics, and for the benefit of those who are not versed in the econometric jargon, this handy summary gives the gist of it in plain English:

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.

When things sound to good to be true, they usually aren’t. And that goes for econometric wet dreams too. The snag is, as Leamer convincingly argues, 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 pretty little 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 without support, but a belief *impossible* to support. The theories we work with when building our econometric 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 we choose to put into our models.

*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 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 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, strictly seen, invalid.

Econometrics is basically a deductive method. Given the assumptions (such as manipulability, transitivity, separability, additivity, linearity, etc) 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. That also applies to econometrics.

## Econometrics — the danger of calling your pet cat a dog

22 Oct, 2019 at 12:13 | Posted in Statistics & Econometrics | 2 CommentsSince econometrics doesn’t content itself with only making optimal predictions, but also aspires to explain things in terms of causes and effects, econometricians need loads of assumptions — most important of these are additivity and linearity. Important, simply because if they are not true, your model is invalid and descriptively incorrect. And when the model is wrong — well, then it’s wrong.

The assumption of additivity and linearity means that the outcome variable is, in reality, linearly related to any predictors … and that if you have several predictors then their combined effect is best described by adding their effects together …

This assumption is the most important because if it is not true then even if all other assumptions are met, your model is invalid because you have described it incorrectly. It’s a bit like calling your pet cat a dog: you can try to get it to go in a kennel, or to fetch sticks, or to sit when you tell it to, but don’t be surprised when its behaviour isn’t what you expect because even though you’ve called it a dog, it is in fact a cat. Similarly, if you have described your statistical model inaccurately it won’t behave itself and there’s no point in interpreting its parameter estimates or worrying about significance tests of confidence intervals: the model is wrong.

Our admiration for technical virtuosity should not blind us to the fact that we have to have a cautious attitude towards probabilistic inferences in economic contexts. Science should — as Keynes once put it — help us penetrate to “the true process of causation lying behind current events” and disclose “the causal forces behind the apparent facts.” We should look out for causal relations, but econometrics can never be more than a starting point in that endeavour since econometric (statistical) explanations are not explanations in terms of mechanisms, powers, capacities or causes. Firmly stuck in an empiricist tradition, econometrics is only concerned with the measurable aspects of reality. But there is always the possibility that there are other variables — of vital importance and perhaps unobservable and non-additive — that were not considered for the model. Those who were can hence never be guaranteed to be more than potential causes, and not real causes. 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. To warrant that 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. They seldom do.

## Econometrics — junk science with no relevance whatsoever to real-world economics

17 Oct, 2019 at 09:47 | Posted in Statistics & Econometrics | 3 CommentsDo you believe that 10 to 20% of the decline in crime in the 1990s was caused by an increase in abortions in the 1970s? Or that the murder rate would have increased by 250% since 1974 if the United States had not built so many new prisons? Did you believe predictions that the welfare reform of the 1990s would force 1,100,000 children into poverty?

If you were misled by any of these studies, you may have fallen for a pernicious form of junk science: the use of mathematical modeling to evaluate the impact of social policies. These studies are superficially impressive. Produced by reputable social scientists from prestigious institutions, they are often published in peer reviewed scientific journals. They are filled with statistical calculations too complex for anyone but another specialist to untangle. They give precise numerical “facts” that are often quoted in policy debates. But these “facts” turn out to be will o’ the wisps …

These predictions are based on a statistical technique called multiple regression that uses correlational analysis to make causal arguments … The problem with this, as anyone who has studied statistics knows, is that correlation is not causation. A correlation between two variables may be “spurious” if it is caused by some third variable. Multiple regression researchers try to overcome the spuriousness problem by including all the variables in analysis. The data available for this purpose simply is not up to this task, however, and the studies have consistently failed.

Mainstream economists often hold the view that if you are critical of econometrics it can only be because you are a sadly misinformed and misguided person who dislike and do not understand much of it.

As Goertzel’s eminent article shows, this is, however, nothing but a gross misapprehension.

And just as Goertzel, Keynes certainly did not misunderstand the crucial issues at stake in his critique of econometrics. Quite the contrary. He knew them all too well — and was not satisfied with the validity and philosophical underpinnings of the assumptions made for applying its methods.

Keynes’ critique is still valid and unanswered in the sense that the problems he pointed at are still with us today and ‘unsolved.’ Ignoring them — the most common practice among applied econometricians — is not to solve them.

To apply statistical and mathematical methods to the real-world economy, the econometrician has to make some quite strong assumptions. In a review of Tinbergen’s econometric work — published in *The Economic Journal* in 1939 — Keynes gave a comprehensive critique of Tinbergen’s work, focusing on the limiting and unreal character of the assumptions that econometric analyses build on:

**Completeness**: Where Tinbergen attempts to specify and quantify which different factors influence the business cycle, Keynes maintains there has to be a complete list of *all* the relevant factors to avoid misspecification and spurious causal claims. Usually, this problem is ‘solved’ by econometricians assuming that they somehow have a ‘correct’ model specification. Keynes is, to put it mildly, unconvinced:

It will be remembered that the seventy translators of the Septuagint were shut up in seventy separate rooms with the Hebrew text and brought out with them, when they emerged, seventy identical translations. Would the same miracle be vouchsafed if seventy multiple correlators were shut up with the same statistical material? And anyhow, I suppose, if each had a different economist perched on his

a priori, that would make a difference to the outcome.

**Homogeneity**: To make inductive inferences possible — and being able to apply econometrics — the system we try to analyse has to have a large degree of ‘homogeneity.’ According to Keynes most social and economic systems — especially from the perspective of real historical time — lack that ‘homogeneity.’ As he had argued already in *Treatise on Probability*, it wasn’t always possible to take repeated samples from a fixed population when we were analysing real-world economies. In many cases, there simply are no reasons at all to assume the samples to be homogenous. Lack of ‘homogeneity’ makes the principle of ‘limited independent variety’ non-applicable, and hence makes inductive inferences, strictly seen, impossible since one of its fundamental logical premises are not satisfied. Without “much repetition and uniformity in our experience” there is no justification for placing “great confidence” in our inductions.

And then, of course, there is also the ‘reverse’ variability problem of non-excitation: factors that do not change significantly during the period analysed, can still very well be extremely important causal factors.

**Stability:** Tinbergen assumes there is a stable spatio-temporal relationship between the variables his econometric models analyze. But as Keynes had argued already in his *Treatise on Probability* it was not really possible to make inductive generalisations based on correlations in one sample. As later studies of ‘regime shifts’ and ‘structural breaks’ have shown us, it is exceedingly difficult to find and establish the existence of stable econometric parameters for anything but rather short time series.

**Measurability:** Tinbergen’s model assumes that all relevant factors are measurable. Keynes questions if it is possible to adequately quantify and measure things like expectations and political and psychological factors. And more than anything, he questioned — both on epistemological and ontological grounds — that it was always and everywhere possible to measure real-world uncertainty with the help of probabilistic risk measures. Thinking otherwise can, as Keynes wrote, “only lead to error and delusion.”

**Independence**: Tinbergen assumes that the variables he treats are independent (still a standard assumption in econometrics). Keynes argues that in such a complex, organic and evolutionary system as an economy, independence is a deeply unrealistic assumption to make. Building econometric models from that kind of simplistic and unrealistic assumptions risk producing nothing but spurious correlations and causalities. Real-world economies are organic systems for which the statistical methods used in econometrics are ill-suited, or even, strictly seen, inapplicable. Mechanical probabilistic models have little leverage when applied to non-atomic evolving organic systems — such as economies.

It is a great fault of symbolic pseudo-mathematical methods of formalising a system of economic analysis … that they expressly assume strict independence between the factors involved and lose all their cogency and authority if this hypothesis is disallowed; whereas, in ordinary discourse, where we are not blindly manipulating but know all the time what we are doing and what the words mean, we can keep “at the back of our heads” the necessary reserves and qualifications and the adjustments which we shall have to make later on, in a way in which we cannot keep complicated partial differentials “at the back” of several pages of algebra which assume that they all vanish.

Building econometric models can’t be a goal in itself. Good econometric models are means that make it possible for us to infer things about the real-world systems they ‘represent.’ If we can’t show that the mechanisms or causes that we isolate and handle in our econometric models are ‘exportable’ to the real world, they are of limited value to our understanding, explanations or predictions of real-world economic systems.

The kind of fundamental assumption about the character of material laws, on which scientists appear commonly to act, seems to me to be much less simple than the bare principle of uniformity. They appear to assume something much more like what mathematicians call the principle of the superposition of small effects, or, as I prefer to call it, in this connection, the

atomiccharacter of natural law. The system of the material universe must consist, if this kind of assumption is warranted, of bodies which we may term (without any implication as to their size being conveyed thereby)legal atoms, such that each of them exercises its own separate, independent, and invariable effect, a change of the total state being compounded of a number of separate changes each of which is solely due to a separate portion of the preceding state …Yet if different wholes were subject to laws

quawholes and not simply on account of and in proportion to the differences of their parts, knowledge of a part could not lead, it would seem, even to presumptive or probable knowledge as to its association with other parts.

**Linearity:** To make his models tractable, Tinbergen assumes the relationships between the variables he study to be linear. This is still standard procedure today, but as Keynes writes:

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.

To Keynes, it was a ‘fallacy of reification’ to assume that all quantities are additive (an assumption closely linked to independence and linearity).

The unpopularity of the principle of organic unities shows very clearly how great is the danger of the assumption of unproved additive formulas. The fallacy, of which ignorance of organic unity is a particular instance, may perhaps be mathematically represented thus: suppose f(x) is the goodness of x and f(y) is the goodness of y. It is then assumed that the goodness of x and y together is f(x) + f(y) when it is clearly f(x + y) and only in special cases will it be true that f(x + y) = f(x) + f(y). It is plain that it is never legitimate to assume this property in the case of any given function without proof.

J. M. Keynes “Ethics in Relation to Conduct” (1903)

And as even one of the founding fathers of modern econometrics — Trygve Haavelmo — wrote:

What is the use of testing, say, the significance of regression coefficients, when maybe, the whole assumption of the linear regression equation is wrong?

Real-world social systems are usually not governed by stable causal mechanisms or capacities. The kinds of ‘laws’ and relations that econometrics has established, are laws and relations about entities in models that presuppose causal mechanisms and variables — and the relationship between them — being linear, additive, homogenous, stable, invariant and atomistic. But — when causal mechanisms operate in the real world they only do it in ever-changing and unstable combinations where the whole is more than a mechanical sum of parts. Since statisticians and econometricians — as far as I can see — haven’t been able to convincingly warrant their assumptions of homogeneity, stability, invariance, independence, additivity as being ontologically isomorphic to real-world economic systems, Keynes’ critique is still valid. As long as — as Keynes writes in a letter to Frisch in 1935 — “nothing emerges at the end which has not been introduced expressively or tacitly at the beginning,” I remain doubtful of the scientific aspirations of econometrics.

In his critique of Tinbergen, Keynes points us to the fundamental logical, epistemological and ontological problems of applying statistical methods to a basically unpredictable, uncertain, complex, unstable, interdependent, and ever-changing social reality. Methods designed to analyse repeated sampling in controlled experiments under fixed conditions are not easily extended to an organic and non-atomistic world where time and history play decisive roles.

Econometric modelling should never be a substitute for thinking. From that perspective, it is really depressing to see how much of Keynes’ critique of the pioneering econometrics in the 1930s-1940s is still relevant today. And that is also a reason why we — as does Goertzl — have to keep on criticizing it.

The general line you take is interesting and useful. It is, of course, not exactly comparable with mine. I was raising the logical difficulties. You say in effect that, if one was to take these seriously, one would give up the ghost in the first lap, but that the method, used judiciously as an aid to more theoretical enquiries and as a means of suggesting possibilities and probabilities rather than anything else, taken with enough grains of salt and applied with superlative common sense, won’t do much harm. I should quite agree with that. That is how the method ought to be used.

Keynes, letter to E.J. Broster, December 19, 1939

Blog at WordPress.com.

Entries and comments feeds.