Confusing statistics with research

10 April, 2017 at 16:13 | Posted in Economics | Comments Off on Confusing statistics with research

140113.bigdataCoupled 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.

Dudley Duncan

A standard view among econometricians and other economists working with statistical models, is that their models are only in the mind. From a realist point of view, that is a rather untenable view. The reason we as scientists are interested in things is that they are parts of the way the world works. We represent the workings of things in the real world by means of models, but that doesn’t mean that things aren’t facts pertaining to relations and structures that exist in the real world. If they were only “in the mind,” most of us wouldn’t care less.

The econometricians’ nominalist-positivist view of science and models — the belief that science can only deal with observable regularity patterns of a more or less lawlike kind — implies that only data matters. Trying to go beyond observed data in search of the underlying real factors and relations that generate the data is not admissible. The real factors and relations according to the econometric methodology are beyond reach since they allegedly are both unobservable and unmeasurable. This also means that instead of treating the model-based findings as interesting clues for digging deepeer into real structures and mechanisms, they are treated as the end points of the investigation.

If econometrics is to progress, it has to abandon its outdated nominalist-positivist view of science and the belief that science can only deal with observable regularity patterns of a more or less law-like kind. Scientific theories ought to do more than just describe event-regularities and patterns — they also have to analyze and describe the mechanisms, structures, and processes that give birth to these patterns and eventual regularities.

The marginal return on its ever higher technical sophistication in no way makes up for the lack of serious under-labouring of the deeper philosophical and methodological foundations of mainstream economics. 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.

A rigorous application of statistical and econometric methods in economics really presupposes that the phenomena of our real world economies are ruled by stable causal relations between variables. But real world social systems are not governed by stable causal mechanisms or capacities! That rather embarrassing fact makes most of the achievements of econometrics — as most of contemporary endeavours of mainstream economic statistical modeling — rather useless.

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