Last month The New York Times featured an article about Dr. Doom: Economics professor “Roubini, a respected but formerly obscure academic, has become a major figure in the public debate about the economy: the seer who saw it coming.”
This article caught my statistician eye due to the description of “data” and “models”. While economists in the article portray Roubini as not using data and econometric models, a careful read shows that he actually does use data and models, but perhaps unusual data and unusual models!
Here are two interesting quotes:
“When I weigh evidence,” he told me, “I’m drawing on 20 years of accumulated experience using models” — but his approach is not the contemporary scholarly ideal in which an economist builds a model in order to constrain his subjective impressions and abide by a discrete set of data.
Later on, Roubini is quoted:
“After analyzing the markets that collapsed in the ’90s, Roubini set out to determine which country’s economy would be the next to succumb to the same pressures.”
This might not be data mining per-se, but note that Roubini’s approach is at heart similar to the data mining approach: looking at unusual data (here, taking an international view rather than focus on national only) and finding patterns within them that predict economic downfalls. In a standard data mining framework we would of course include also all those markets that have not-collapsed, and then set up the problem as a “direct marketing” problem: who is most likely to fall?
A final note: As a strong believer in the difference between the goals of explaining and forecasting, I think that econometricians should stop limiting their modeling to explanatory, causality-based models. Good forecasters might not be revealing in terms of causality, but in many cases their forecasts will be far more accurate than those from explanatory models!