Home > Uncategorized > Keynes’ critique of econometrics — the nodal point

Keynes’ critique of econometrics — the nodal point

from Lars Syll

In treatprob-2my judgment, the practical usefulness of those modes of inference, here termed Universal and Statistical Induction, on the validity of which the boasted knowledge of modern science depends, can only exist—and I do not now pause to inquire again whether such an argument must be circular—if the universe of phenomena does in fact present those peculiar characteristics of atomism and limited variety which appear more and more clearly as the ultimate result to which material science is tending …

The physicists of the nineteenth century have reduced matter to the collisions and arrangements of particles, between which the ultimate qualitative differences are very few …

The validity of some current modes of inference may depend on the assumption that it is to material of this kind that we are applying them … Professors of probability have been often and justly derided for arguing as if nature were an urn containing black and white balls in fixed proportions. Quetelet once declared in so many words—“l’urne que nous interrogeons, c’est la nature.” But again in the history of science the methods of astrology may prove useful to the astronomer; and it may turn out to be true—reversing Quetelet’s expression—that “La nature que nous interrogeons, c’est une urne”.

Professors of probability and statistics, yes. And more or less every mainstream economist!

The standard view in statistics – and the axiomatic probability theory underlying it – is to a large extent based on the rather simplistic idea that ‘more is better.’ But as Keynes argues – ‘more of the same’ is not what is important when making inductive inferences. It’s rather a question of ‘more but different.’  

Variation, not replication, is at the core of induction. Finding that p(x|y) = p(x|y & w) doesn’t make w ‘irrelevant.’ Knowing that the probability is unchanged when w is present gives p(x|y & w) another evidential weight (‘weight of argument’). Running 10 replicative experiments do not make you as ‘sure’ of your inductions as when running 10 000 varied experiments – even if the probability values happen to be the same.

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’ 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.’

Science according to Keynes should help us penetrate to “the true process of causation lying behind current events” and disclose “the causal forces behind the apparent facts.” Models can never be more than a starting point in that endeavour. He further argued that it was inadmissible to project history on the future. Consequently we cannot presuppose that what has worked before, will continue to do so in the future. That statistical models can get hold of correlations between different ‘variables’ is not enough. If they cannot get at the causal structure that generated the data, they are not really ‘identified.’

In his critique of Tinbergen, Keynes comes back to these fundamental logical, epistemological and ontological problems of applying statistical methods — based on probabilistic axiomatics — 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.

The naive pretence that we as social scientists can just walk in to the library and grab a statistical model from the shelf and apply it to an open and mutable social reality has to be forsaken. As social scientists we always have to argue for and justify the belief in the appropriateness in choosing to work with specific methods and models in a given patio-temporal context — and why we should believe in the results produced.

The inductive and causal claims that can be made when applying methods appropriate to intrinsically time-less closed ‘nomological machines’ on open social systems moving in real historical time, are indeed limited.

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