by What can economists know? The early concerns voiced by such critics as Keynes and Hayek, while they may indeed have been exaggerated, were not misplaced. I believe that much of the difficulty economists have encountered over the past fifty years can be traced to the fact that the economic environment we seek to model are sometimes too messy to be fitted into the mold of a well-behaved, complete model of the standard kind. It is not generally the case that some sharp dividing line separates a set of important systematic influences that we can measure, proxy, or control for, from the many small unsystematic influences that we can bundle into a ‘noise’ term. So when we set out to test economic theories in the framework of the standard paradigm, we face quite serious and deep-seated difficulties. The problem of model selection may be such that the embedded test ends up being inconclusive, or unpersuasive. Sutton’s Gaston Eyskens Lectures forcefully show what a gross misapprehension it is to — as most mainstream economists today — hold the view that criticisms of econometrics are the conclusions of sadly misinformed and misguided people who dislike and do not understand much of it. To be careful and cautious is not the same as to dislike.
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What can economists know?
The early concerns voiced by such critics as Keynes and Hayek, while they may indeed have been exaggerated, were not misplaced.
I believe that much of the difficulty economists have encountered over the past fifty years can be traced to the fact that the economic environment we seek to model are sometimes too messy to be fitted into the mold of a well-behaved, complete model of the standard kind. It is not generally the case that some sharp dividing line separates a set of important systematic influences that we can measure, proxy, or control for, from the many small unsystematic influences that we can bundle into a ‘noise’ term. So when we set out to test economic theories in the framework of the standard paradigm, we face quite serious and deep-seated difficulties. The problem of model selection may be such that the embedded test ends up being inconclusive, or unpersuasive.
Sutton’s Gaston Eyskens Lectures forcefully show what a gross misapprehension it is to — as most mainstream economists today — hold the view that criticisms of econometrics are the conclusions of sadly misinformed and misguided people who dislike and do not understand much of it. To be careful and cautious is not the same as to dislike. As any perusal of the mathematical-statistical and philosophical works of people like for example David Freedman, Rudolf Kalman, John Maynard Keynes, and Tony Lawson show, the critique is put forward by respected authorities. I would argue, against “common knowledge” and in line with Sutton, that they do not misunderstand the crucial issues at stake in the development of econometrics. Quite the contrary. They know them all too well — and are not satisfied with the validity and philosophical underpinning of the assumptions made for applying its methods.
Although advances have been made using a modern empiricist approach in modern econom(etr)ics, there are still some unsolved “problematics” with its epistemological and ontological presuppositions. There is, e. g., an implicit assumption that the data generating process (DGP) fundamentally has an invariant property and that models that are structurally unstable just have not been able to get hold of that invariance. But, as already Keynes maintained, one cannot just presuppose or take for granted that kind of invariance. It has to be argued and justified. Grounds have to be given for viewing reality as satisfying conditions of model-closure. It is as if the lack of closure that shows up in the form of structurally unstable models somehow could be solved by searching for more autonomous and invariable “atomic uniformity”. But if reality is “congruent” to this analytical prerequisite has to be argued for, and not simply taken for granted.
Even granted that closures come in degrees, we should not compromise on ontology. Some methods simply introduce improper closures, closures that make the disjuncture between models and real world target systems inappropriately large. Garbage in, garbage out.
Underlying the search for these immutable “fundamentals” lays the implicit view of the world as consisting of material entities with their own separate and invariable effects. These entities are thought of as being able to be treated as separate and addible causes, thereby making it possible to infer complex interaction from knowledge of individual constituents with limited independent variety. But if this is a justified analytical procedure cannot be answered without confronting it with the nature of the objects the models are supposed to describe, explain or predict. Keynes himself thought it generally inappropriate to apply the “atomic hypothesis” to such an open and “organic entity” as the real world. As far as I can see these are still appropriate strictures all econometric approaches have to face. Grounds for believing otherwise have to be provided by the econometricians.
Trygve Haavelmo, the “father” of modern probabilistic econometrics, wrote that he and other econometricians could not “build a complete bridge between our models and reality” by logical operations alone, but finally had to make “a non-logical jump.” A part of that jump consisted in that econometricians “like to believe … that the various a priori possible sequences would somehow cluster around some typical time shapes, which if we knew them, could be used for prediction.” But why the “logically conceivable” really should turn out to be the case is difficult to see. At least if we are not satisfied by sheer hope. Keynes, as already noted, reacted against using unargued for and unjustified assumptions of complex structures in an open system being reducible to those of individuals. In real economies it is unlikely that we find many “autonomous” relations and events.