Home > Uncategorized > Econometrics — a Keynesian perspective

Econometrics — a Keynesian perspective

from Lars Syll

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.

J M Keynes

Mainstream economists today usually subscribe to the idea that although mathematical-statistical models are not ‘always the right guide for policy,’ they are  still somehow necessary for making policy recommendations. The models are supposed to supply us with a necessary ‘discipline of thinking.’

This emphasis on the value of modeling should come as no surprise. Mainstreamers usually vehemently defend the formalization and mathematization that comes with the insistence of using a model building strategy in economics. 

But if these math-is-the-message-modelers aren’t able to show that the mechanisms or causes that they isolate and handle in their mathematical-statistically formalised models are stable in the sense that they do not change when we ‘export’ them to our ‘target systems,’ these models do only hold under ceteris paribus conditions and are consequently of limited value to our understandings, explanations or predictions of real economic systems. Building models only to show  ‘self-dicipline’ is setting the aspiration level far too low.

According to Keynes, science should 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 models — mathematical, econometric, or what have you — can never be more than a starting point in that endeavour. There is always the possibility that there are other (non-quantifiable) variables – of vital importance and although perhaps unobservable and non-additive not necessarily epistemologically inaccessible – that were not considered for the formalized mathematical model.

The kinds of laws and relations that ‘modern’ economics has established, are laws and relations about mathematically formalized 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 mathematical-statistical ‘nomological machines’ they are rare, or even non-existant. Whether econometric or not, that also, unfortunately, makes most of contemporary mainstream endeavours of economic modeling rather useless.

Econometric modeling 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.

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

  1. May 31, 2016 at 6:15 am

    There is a problem here with Keynes’ position. Some scientists and engineers (electrical, climate, cognitive, etc.) base much of the analyses they perform and recommendations they make on computer modeling, and most sciences use models as the basis for at some of their work. While Keynes may be correct that currently modeling in economics is best used as “a means of suggesting possibilities and probabilities rather than anything else” and “taken with enough grains of salt and applied with superlative common sense, won’t do much harm.” But under these conditions it won’t do much good either. Modeling can be a useful tool for any science, including economics. How do we make it so for economics?

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