I tweeted this link, and it's getting far, far more retweets than I would have expected, so I thought I'd note it here:
Econometrics and "Big Data", by Dave Giles: In this age of "big data" there's a whole new language that econometricians need to learn. ... What do you know about such things as:
- Decision trees
- Support vector machines
- Neural nets
- Deep learning
- Classification and regression trees
- Random forests
- Penalized regression (e.g., the lasso, lars, and elastic nets)
- Spike and slab regression?
Probably not enough!
If you want some motivation to rectify things, a recent paper by Hal Varian ... titled, "Big Data: New Tricks for Econometrics" ... provides an extremely readable introduction to several of these topics.
He also offers a valuable piece of advice:
"I believe that these methods have a lot to offer and should be more widely known and used by economists. In fact, my standard advice to graduate students these days is 'go to the computer science department and take a class in machine learning'."