. A classic textbook example of the ‘Table 2 Fallacy’ in economics arises when estimating the return to education and misinterpreting regression coefficients. Suppose an economist wishes to estimate the causal effect of an additional year of schooling on earnings and estimates If the estimated coefficient is small and statistically insignificant, the economist might conclude […]
Statistics & Econometrics
Despite the pervasive uncertainties surrounding their assumptions, economic modellers frequently confine their expressions of uncertainty to those generated from within the very assumptions they have chosen to embed in their models — thereby creating a false sense of precision while ignoring deeper sources of ignorance. Econometrics offers a series of cautionary tales in which highly […]
In The Book of Why, Judea Pearl puts forward several compelling reasons why the now so popular causal graph-theoretic approach is to be preferred over more traditional regression-based explanatory models. One reason is that causal graphs are non-parametric and therefore do not need to assume, for example, additivity and/or the absence of interaction effects — […]
Taken as a measure of causal explanatory power, R squared does not fare any better. The problem of explaining variances rather than levels shows up here as well—if it measures causal influence, it has to be influences on variances. But we often do not care about the causes of variance in economic variables but instead […]
In simple (and multiple) regression analysis for cross-sectional data, researchers often estimate regressions such as “regress test score (y) on study hours (x)” and obtain a result of the form y = constant + slope coefficient × x + error term. When speaking of increases or decreases in x in these interpretations, we must remember […]
It is well known that even experienced scientists routinely misinterpret p-values in all sorts of ways, including confusion of statistical and practical significance, treating non-rejection as acceptance of the null hypothesis, and interpreting the p-value as some sort of replication probability or as the posterior probability that the null hypothesis is true … It is […]
After mastering the technicalities of regression analysis and econometrics, students often feel as though they are masters of the universe. I usually bring them back down to earth by assigning Christopher Achen’s modern classic Interpreting and Using Regression. This tends to put them back on track, helping them to understand that “no increase in methodological […]
Ed Leamer transformed economists’ understanding of empirical evidence with his landmark 1988 paper, Let’s Take the Con Out of Econometrics. In it, he challenged the profession’s fixation on ‘statistical significance’, describing much empirical research as “measuring with a rubber ruler.” Leamer’s central claim was that complex econometric models depend heavily on hidden, subjective decisions made […]
Mainstream economists often hold the view that Keynes’s criticism of econometrics was the result of a profoundly mistaken thinker who disliked and largely failed to understand it. This, however, is 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 […]
Since econometrics doesn’t content itself with only making optimal predictions, but also aspires to explain things in terms of causes and effects, econometricians need loads of assumptions — most important of these are additivity and linearity. Important, simply because if they are not true, your model is invalid and descriptively incorrect. It’s like calling your house a bicycle. No matter […]