ML and Metrics VII: Cross-Section Non-Linearities

[Click on "Machine Learning" at right for earlier "Machine Learning and Econometrics" posts.]

The predictive modeling perspective needs not only to be respected and embraced in econometrics (as it routinely is, notwithstanding the Angrist-Pischke revisionist agenda), but also to be enhanced by incorporating elements of statistical machine learning (ML). This is particularly true for cross-section econometrics insofar as time-series econometrics is already well ahead in that regard.  For example, although flexible non-parametric ML approaches to estimating conditional-mean functions don't add much to time-series econometrics, they may add lots to cross-section econometric regression and classification analyses, where conditional mean functions may be highly nonlinear for a variety of reasons.  Of course econometricians are well aware of traditional non-parametric issues/approaches, especially kernel and series methods, and they have made many contributions, but there's still much more to be learned from ML.