[Click on "Machine Learning" at right for earlier "Machine Learning and Econometrics" posts.]
We econometricians need -- and have always had -- cross section and time series ("micro econometrics" and "macro/financial econometrics"), causal estimation and predictive modeling, structural and non-structural. And all continue to thrive.
But there's a new twist, happening now, making this an unusually exciting time in econometrics. Predictive econometric modeling is not only alive and well, but also blossoming anew, this time at the interface of micro-econometrics and machine learning. A fine example is the new Kleinberg, Lakkaraju, Leskovic, Ludwig and Mullainathan paper, “Human Decisions and Machine Predictions”, NBER Working Paper 23180 (February 2017).
Good predictions promote good decisions, and econometrics is ultimately about helping people to make good decisions. Hence the new developments, driven by advances in machine learning, are most welcome contributions to a long and distinguished predictive econometric modeling tradition.