Machine Learning vs. Econometrics, II

My last post focused on one key distinction between machine learning (ML) and econometrics (E):   non-causal ML prediction vs. causal E prediction.  I promised later to highlight another, even more important, distinction.  I'll get there in the next post.

But first let me note a key similarity.  ML vs. E in terms of non-causal vs. causal prediction is really only comparing ML to "half" of E (the causal part).  The other part of E (and of course statistics, so let's call it E/S), going back a century or so, focuses on non-causal prediction, just like ML.  The leading example is time-series E/S.  Just take a look at an E/S text like Elliott and Timmermann (contents and first chapter here; index here).  A lot of it looks like parts of ML.  But it's not "E/S people chasing ML ideas"; rather, E/S has been in the game for decades, often well ahead of ML.

For this reason the E/S crowd sometimes wonders whether "ML" and "data science" are just the same old wine in a new bottle.  (The joke goes, Q: What is a "data scientist"?  A: A statistician who lives in San Francisco.)  ML/DataScience is not the same old wine, but it's a blend, and a significant part of the blend is indeed E/S.

To be continued...