Tag Archives: Statistics & Econometrics

Keynes’ devastating critique of econometrics Mainstream economists often hold the view that Keynes’ criticism of econometrics was the result of a sadly misinformed and misguided person who disliked and did not understand much of it. This is, however, nothing but a gross misapprehension. To be careful and cautious is not the same as to dislike. Keynes did not misunderstand the crucial issues at stake in the development of econometrics. Quite the contrary. He...

Which independent variables should be included in the equation? The goal is a “good fit” … How can a good fit be recognized? A popular measure for the satisfactoriness of a regression is the coefficient of determination, R2. If this number is large, it is said, the regression gives a good fit … Nothing about R2 supports these claims. This statistic is best regarded as characterizing the geometric shape of the regression points and not much more. The central difficulty with R2...

Big Data — poor science Almost everything we do these days leaves some kind of data trace in some computer system somewhere. When such data is aggregated into huge databases it is called “Big Data”. It is claimed social science will be transformed by the application of computer processing and Big Data. The argument is that social science has, historically, been “theory rich” and “data poor” and now we will be able to apply the methods of “real science” to...

On the non-applicability of statistical theory Eminent statistician David Salsburg is rightfully very critical of the way social scientists — including economists and econometricians — uncritically and without arguments have come to simply assume that one can apply probability distributions from statistical theory on their own area of research: We assume there is an abstract space of elementary things called ‘events’ … If a measure on the abstract space of...

Regression to the mean Regression to the men is nothing but the universal truth of the fact that whenever we have an imperfect correlation between two scores, we have regression to the mean. [embedded content]

How to do econometrics properly Always, but always, plot your data. Remember that data quality is at least as important as data quantity. Always ask yourself, “Do these results make economic/common sense”? Check whether your “statistically significant” results are also “numerically/economically significant”. Be sure that you know exactly what assumptions are used/needed to obtain the results relating to the properties of any estimator or test that you...

Keynes’ critique of econometrics — as valid today as it was in 1939 Renowned ‘error-statistician’ Aris Spanos maintains — in a comment on this blog a couple of weeks ago — that Keynes’ critique of econometrics and the reliability of inferences made when it is applied, “have been addressed or answered.” One could, of course, say that, but the valuation of the statement hinges completely on what we mean by a question or critique being ‘addressed’ or...

On the non-validity of incremental validity A common goal of statistical analysis in the social sciences is to draw inferences about the relative contributions of different variables to some outcome variable. When regressing academic performance, political affiliation, or vocabulary growth on other variables, researchers often wish to determine which variables matter to the prediction and which do not—typically by considering whether each variable’s...

Lindeberg-Levy CLT (student stuff)  [embedded content]

The wide conviction of the superiority of the methods of the science has converted the econometric community largely to a group of fundamentalist guards of mathematical rigour. It is often the case that mathemical rigour is held as the dominant goal and the criterion for research topic choice as well as research evaluation, so much so that the relevance of the research to business cycles is reduced to empirical illustrations. To that extent, probabilistic formalization has...