Important and far-reaching problems still beset regression analysis and econometrics — many of which basically is a result of unsustainable ontological views.

complex-research-terminology-simMost econometricians have a nominalist-positivist view of science and models, according to which science can only deal with observable regularity patterns of a more or less lawlike kind. Only data matters and trying to (ontologically) go beyond observed data in search of underlying real factors and relations that generate the data is not admissible. All have to take place in the model of the econometric mind, since the real factors and relations according to the econometric (epistemologically based) 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 deeper into real structures and mechanisms, they are treated as the endpoints of the investigation.

As mathematical statistician David Freedman writes in Statistical Models and Causal Inference (2010):

In my view, regression models are not a particularly good way of doing empirical work in the social sciences today, because the technique depends on knowledge that we do not have. Investigators who use the technique are not paying adequate attention to the connection – if any – between the models and the phenomena they are studying. Their conclusions may be valid for the computer code they have created, but the claims are hard to transfer from that microcosm to the larger world …

freedman2Given the limits to present knowledge, I doubt that models can be rescued by technical fixes. Arguments about the theoretical merit of regression or the asymptotic behavior of specification tests for picking one version of a model over another seem like the arguments about how to build desalination plants with cold fusion and the energy source. The concept may be admirable, the technical details may be fascinating, but thirsty people should look elsewhere …

Causal inference from observational data presents may difficulties, especially when underlying mechanisms are poorly understood. There is a natural desire to substitute intellectual capital for labor, and an equally natural preference for system and rigor over methods that seem more haphazard. These are possible explanations for the current popularity of statistical models.

Indeed, far-reaching claims have been made for the superiority of a quantitative template that depends on modeling – by those who manage to ignore the far-reaching assumptions behind the models. However, the assumptions often turn out to be unsupported by the data. If so, the rigor of advanced quantitative methods is a matter of appearance rather than substance.

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 do more than just describe event-regularities and patterns — they also analyze and describe the mechanisms, structures, and processes that give birth to these patterns and eventual regularities.