## Probabilistic econometrics – science without foundations (part I)

21 February, 2012 at 15:31 | Posted in Statistics & Econometrics, Theory of Science & Methodology | 5 CommentsModern probabilistic econometrics relies on the notion of probability. To at all be amenable to econometric analysis, economic observations allegedly have to be conceived as random events.

But is it really necessary to model the economic system as a system where randomness can only be analyzed and understood when based on an *a priori* notion of probability?

*Where do probabilities come from?*

In probabilistic econometrics, events and observations are as a rule interpreted as random variables as if generated by an underlying probability density function, and a fortiori – since probability density functions are only definable in a probability context – consistent with a probability. As Haavelmo (1944:iii) has it:

For no tool developed in the theory of statistics has any meaning – except , perhaps for descriptive purposes – without being referred to some stochastic scheme.

When attempting to convince us of the necessity of founding empirical economic analysis on probability models, Haavelmo – building largely on the earlier Fisherian paradigm – actually forces econometrics to (implicitly) interpret events as random variables generated by an underlying probability density function.

This is at odds with reality. Randomness obviously is a fact of the real world. Probability, on the other hand, attaches to the world via intellectually constructed models, and *a fortiori* is only a fact of a probability generating machine or a well constructed experimental arrangement or “chance set-up”.

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* or *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 element. 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) 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!

From a realistic point of view we really have to admit that the socio-economic states of nature that we talk of in most social sciences – and certainly in econometrics – are not amenable to analyze as probabilities, simply because in the real world open systems that social sciences – including econometrics – analyze, there are no probabilities to be had!

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

If we agree on this, we also have to admit that probabilistic econometrics lacks a sound justification. I would even go further and argue that there really is no justifiable rationale at all for this belief that all economically relevant data can be adequately captured by a probability measure. In most real world contexts one has to *argue* one’s case. And that is obviously something seldom or never done by practitioners of probabilistic econometrics.

*What is randomness?*

Econometrics and probability are intermingled with randomness. But what is randomness?

In probabilistic econometrics it is often defined with the help of independent trials – two events are said to be independent if the occurrence or nonoccurrence of either one has no effect on the probability of the occurrence of the other – as drawing cards from a deck, picking balls from an urn, spinning a roulette wheel or tossing coins – trials which are only definable if somehow set in a probabilistic context.

But if we pick a sequence of prices – say 2, 4, 3, 8, 5, 6, 6 – that we want to use in an econometric regression analysis, how do we know the sequence of prices is random and *a fortiori* being able to treat as generated by an underlying probability density function? How can we argue that the sequence is a sequence of probabilistically independent random prices? And are they really random in the sense that is most often applied in probabilistic econometrics – where X is called a random variable only if there is a sample space S with a probability measure and X is a real-valued function over the elements of S?

Bypassing the scientific challenge of going from describable randomness to calculable probability by just assuming it, is of course not an acceptable procedure. Since a probability density function is a “Gedanken” object that does not exist in a natural sense, it has to come with an export license to our real target system if it is to be considered usable.

Among those who at least honestly try to face the problem – the usual procedure is to refer to some artificial mechanism operating in some “games of chance” of the kind mentioned above and which generates the sequence. But then we still have to show that the real sequence somehow coincides with the ideal sequence that defines independence and randomness within our – to speak with science philosopher Nancy Cartwright (1999) – “nomological machine”, our chance set-up, our probabilistic model.

As the originator of the Kalman filter, Rudolf Kalman (1994:143), notes:

Not being able to test a sequence for ‘independent randomness’ (without being told how it was generated) is the same thing as accepting that reasoning about an “independent random sequence” is not operationally useful.

So why 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 (how many sides do the dice have, are the cards unmarked, etc)

If we do adhere to the Fisher-Haavelmo paradigm of probabilistic econometrics we also have to assume that all noise in our data is probabilistic and that errors are well-behaving, something that is hard to justifiably argue for as a real phenomena, and not just an operationally and pragmatically tractable assumption.

Maybe Kalman’s (1994:147) verdict that

Haavelmo’s error that randomness = (conventional) probability is just another example of scientific prejudice

is, from this perspective seen, not far-fetched.

Accepting Haavelmo’s domain of probability theory and sample space of infinite populations– just as Fisher’s (1922:311) “hypothetical infinite population, of which the actual data are regarded as constituting a random sample”, von Mises’ “collective” or Gibbs’ ”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.

As David Salsburg (2001:146) notes on probability theory:

[W]e assume there is an abstract space of elementary things called ‘events’ … If a measure on the abstract space of events fulfills certain axioms, then it is a probability. To use probability in real life, we have to identify this space of events and do so with sufficient specificity to allow us to actually calculate probability measurements on that space … Unless we can identify [this] abstract space, the probability statements that emerge from statistical analyses will have many different and sometimes contrary meanings.

Just as e. g. Keynes (1921) and Georgescu-Roegen (1971), Salsburg (2001:301f) is very critical of the way social scientists – including economists and econometricians – uncritically and without arguments have come to simply assume that one can apply probability distributions from statistical theory on their own area of research:

Probability is a measure of sets in an abstract space of events. All the mathematical properties of probability can be derived from this definition. When we wish to apply probability to real life, we need to identify that abstract space of events for the particular problem at hand … It is not well established when statistical methods are used for observational studies … If we cannot identify the space of events that generate the probabilities being calculated, then one model is no more valid than another … As statistical models are used more and more for observational studies to assist in social decisions by government and advocacy groups, this fundamental failure to be able to derive probabilities without ambiguity will cast doubt on the usefulness of these methods.

Some wise words that ought to be taken seriously by probabilistic econometricians is also given by mathematical statistician Gunnar Blom (2004:389):

If the demands for randomness are not at all fulfilled, you only bring damage to your analysis using statistical methods. The analysis gets an air of science around it, that it does not at all deserve.

Richard von Mises (1957:103) noted that

Probabilities exist only in collectives … This idea, which is a deliberate restriction of the calculus of probabilities to the investigation of relations between distributions, has not been clearly carried through in any of the former theories of probability.

And obviously not in Haavelmo’s paradigm of probabilistic econometrics either. It would have been better if one had heeded von Mises warning (1957:172) that

the field of application of the theory of errors should not be extended too far.

**This importantly also means that if you cannot show that data satisfies all the conditions of the probabilistic nomological machine – including e. g. the distribution of the deviations corresponding to a normal curve – then the statistical inferences used, lack sound foundations! And this really is the basis of the argument put forward in this essay – probabilistic econometrics lacks sound foundations**.

.

*References*

Gunnar Blom et al: *Sannolikhetsteori och statistikteori med tillämpningar*, Lund: Studentlitteratur.

Cartwright, Nancy (1999), *The Dappled World*. Cambridge: Cambridge University Press.

Fisher, Ronald (1922), On the mathematical foundations of theoretical statistics. *Philosophical Transactions of The Royal Society A*, 222.

Georgescu-Roegen, Nicholas (1971), *The Entropy Law and the Economic Process*. Harvard University Press.

Haavelmo, Trygve (1944), The probability approach in econometrics. *Supplement to Econometrica *12:1-115.

Kalman, Rudolf (1994), Randomness Reexamined. *Modeling, Identification and Control *3:141-151.

Keynes, John Maynard (1973 (1921)), *A Treatise on Probability*. Volume VIII of *The Collected Writings of John Maynard Keynes*, London: Macmillan.

Pålsson Syll, Lars (2007), *John Maynard Keynes*. Stockholm: SNS Förlag.

Salsburg, David (2001), *The Lady Tasting Tea*. Henry Holt.

von Mises, Richard (1957), *Probability, Statistics and Truth*. New York: Dover Publications.

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“This importantly also means that if you cannot show that data satisfies all the conditions of the probabilistic nomological machine – including e. g. the distribution of the deviations corresponding to a normal curve – then the statistical inferences used, lack sound foundations!”

I am struggling to understand why this does not apply to the medical sciences. Does it?

And I am wondering, of course, whether you will decline treatment with, say, chemotherapy (knock on wood) just because the mapping between the event space underlying the statistical analysis has no proven counterpart in reality, and that the inference used to prove the effectiveness of treatment therefore “lack sound foundations”. But let’s just say I doubt it. The money won’t be put where your mouth is.

Comment by pontus— 21 February, 2012 #

As seems to be rather typical of your comments here on my blog Pontus, you change focus from what the article is about. I don’t know if that is an argumentation strategy that you teach your students at Cambridge, but I at least find it somewhat counter-productive. I write a rather theoretical article on the foundations of probability and randomness and its application in modern econometrics, and you ask me a rather pragmatic question on how to evaluate and decide on a question pertaining to medicine. Possibly much of what I write in this artcile do apply to medicine as well, but I cannot have a decided opinion on it, simply because it is beyond my own area of research. And remember, sometimes we simply do not know, as J M Keynes famously had it in his QJE article in 1937:

“By ‘uncertain’ knowledge, let me explain, I do not mean merely to distinguish what is known for certain from what is only probable. The game of roulette is not subject, in this sense, to uncertainty; nor is the prospect of a Victory bond being drawn. Or, again, the expectation of life is only slightly uncertain. Even the weather is only moderately uncertain. The sense in which I am using the term is that in which the prospect of a European war is uncertain, or the price of copper and the rate of interest twenty years hence, or the obsolescence of a new invention, or the position of private wealth owners in the social system, in 1970. About these matters there is no scientific basis on which to form any calculable probability whatever. We simply do not know.

Comment by Lars P Syll— 22 February, 2012 #

Well, typical or not, I am arguing that you are throwing out the baby with the bath water. Randomness in social science, or in science too for that matter, is almost exclusively used to as a measure of our ignorance. The question to ask is therefore not whether an empirical model has an exact counterpart in reality, and whether inference has a strict meaning in the real world, but if it serves as a useful approximation until we know more.

Let’s take a domain you and I know better than medicine; option pricing. You would of course argue, as did Keynes, that the price of options (or copper) is simply unknowable. Yet we have models, the Black and Scholes and its heirs in this example, which provides an impressive probabilistic guidance to accurate pricing. Even out of sample, and in periods long before it was invented. We can blame banks for a lot of misery in recent years, but we can’t blame them for incompetence when it comes to making money. And for that reason if find it very unlikely that your theoretical reasoning would bite if you would try to persuade them that they are doing the wrong thing. As long as the money is flowing in, and as long as deceases are cured, were on the right path.

When the practicality of a certain method serves us so well, why on earth should we abandon it just because we “cannot show that data satisfies all the conditions of the probabilistic nomological machine”?

Comment by pontus— 22 February, 2012 #

[…] of these “solutions” for social sciences in general and economics in specific (see here, here, here and here). And with regards to the present article I think that since the distinction between the […]

Pingback by Judea Pearl on regression and causation | Real-World Economics Review Blog— 28 September, 2013 #

I agree. The analysis goes to far. The whole argument is based on the frequentist notion of probability but a Bayesian would view probability as a useful theory for dealing with ignorance. I think probability theory can be used in this way. However I find that probability in the social sciences is most often used to fit square pegs in round holes. Its a useful technique for getting something out of a garbage theory. That is the problem. It would be better to spend time fixing the garbage theory.

Comment by assman— 12 October, 2015 #