by Econometrics — a second-best explanatory practice While appeal to R squared is a common rhetorical device, it is a very tenuous connection to any plausible explanatory virtues for many reasons. Either it is meant to be merely a measure of predictability in a given data set or it is a measure of causal influence. In either case it does not tell us much about explanatory power. Taken as a measure of predictive power, it is limited in that it predicts variances only. But what we mostly want to predict is levels, about which it is silent. In fact, two models can have exactly the same R squared and yet describe regression lines with very different slopes, the natural predictive measure of levels. Furthermore even in predicting variance, it is entirely
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Econometrics — a second-best explanatory practice
While appeal to R squared is a common rhetorical device, it is a very tenuous connection to any plausible explanatory virtues for many reasons. Either it is meant to be merely a measure of predictability in a given data set or it is a measure of causal influence. In either case it does not tell us much about explanatory power. Taken as a measure of predictive power, it is limited in that it predicts variances only. But what we mostly want to predict is levels, about which it is silent. In fact, two models can have exactly the same R squared and yet describe regression lines with very different slopes, the natural predictive measure of levels. Furthermore even in predicting variance, it is entirely dependent on the variance in the sample—if a covariate shows no variation, then it cannot predict anything. This leads to getting very different measures of explanatory power across samples for reasons not having any obvious connection to explanation.
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 about the causes of levels of those variables about which it is silent. Similarly, because the size of R squared varies with variance in the sample, it can find a large effect in one sample and none in another for arbitrary, noncausal reasons. So while there may be some useful epistemic roles for R squared, measuring explanatory power is not one of them.
Although in a somewhat different context, Jon Elster makes basically the same observation as Kincaid:
Consider two elections, A and B. For each of them, identify the events that cause a given percentage of voters to turn out. Once we have thus explained the turnout in election A and the turnout in election B, the explanation of the difference (if any) follows automatically, as a by-product. As a bonus, we might be able to explain whether identical turnouts in A and B are accidental, that is, due to differences that exactly offset each other, or not. In practice, this procedure might be too demanding. The data or the available theories might not allow us to explain the phenomena “in and of themselves.” We should be aware, however, that if we do resort to explanation of variation, we are engaging in a second-best explanatory practice.
Modern econometrics is fundamentally based on assuming — usually without any explicit justification — that we can gain causal knowledge by considering independent variables that may have an impact on the variation of a dependent variable. As argued by both Kincaid and Elster, this is, however, far from self-evident. Often the fundamental causes are constant forces that are not amenable to the kind of analysis econometrics supplies us with. As Stanley Lieberson has it in Making It Count:
One can always say whether, in a given empirical context, a given variable or theory accounts for more variation than another. But it is almost certain that the variation observed is not universal over time and place. Hence the use of such a criterion first requires a conclusion about the variation over time and place in the dependent variable. If such an analysis is not forthcoming, the theoretical conclusion is undermined by the absence of information …
Moreover, it is questionable whether one can draw much of a conclusion about causal forces from simple analysis of the observed variation … To wit, it is vital that one have an understanding, or at least a working hypothesis, about what is causing the event per se; variation in the magnitude of the event will not provide the answer to that question.
Trygve Haavelmo made a somewhat similar point back in 1941 when criticizing the treatment of the interest variable in Tinbergen’s regression analyses. The regression coefficient of the interest rate variable being zero was according to Haavelmo not sufficient for inferring that “variations in the rate of interest play only a minor role, or no role at all, in the changes in investment activity.” Interest rates may very well play a decisive indirect role by influencing other causally effective variables. And:
the rate of interest may not have varied much during the statistical testing period, and for this reason the rate of interest would not “explain” very much of the variation in net profit (and thereby the variation in investment) which has actually taken place during this period. But one cannot conclude that the rate of influence would be inefficient as an autonomous regulator, which is, after all, the important point.
This problem of ‘nonexcitation’ — when there is too little variation in a variable to say anything about its potential importance, and we can’t identify the reason for the factual influence of the variable being ‘negligible’ — strongly confirms that causality in economics and other social sciences can never solely be a question of statistical inference. Causality entails more than predictability, and to really in-depth explain social phenomena requires theory.
Analysis of variation — the foundation of all econometrics — can never in itself reveal how these variations are brought about. Only when we can tie actions, processes, or structures to the statistical relations detected, can we say that we are getting relevant explanations of causation. Too much in love with axiomatic-deductive modelling, mainstream economists especially tend to forget that accounting for causation — how causes bring about their effects — demands deep subject-matter knowledge and acquaintance with the intricate fabrics and contexts. As Keynes already argued in his A Treatise on Probability, statistics (and econometrics) should primarily be seen as means to describe patterns of associations and correlations, means that we may use as suggestions of possible causal relations. Forgetting that, economists will continue to be stuck with a second-best explanatory practice.