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These methods are not usually taught in economics courses. Why? Not enough data. In most cases economists are dealing with survey data, time series data and p
by dmagee 10y ago
These methods are not usually taught in economics courses.
Why?
Not enough data.
In most cases economists are dealing with survey data, time series data and panel data. The benefits of cross validation and bootstrap/bagging grow with data size. When you're dealing with minimal amounts of messy/misbehaving data these methods lose their power.
Other methods become more important ie: Instrumental Variable estimation, Probit and Tobit models, Vector Autoregressions, Vector Error Correction models. Im sure your econ PhD student friend would know what these are.
A different tool-kit to solve different problems.
- grayclhn 10y ago"Not enough time" and "harder to do inference" are bigger reasons. It's hard to see why cross validation would do worse than the AIC or BIC for lag length selection in VARs, the bootstrap is widely used for inference for all of the models you mentioned, IV isn't known for its exceptional small sample properties, etc. People are working on this stuff, but it takes a little while to get it to work well for Econ research, and it takes some clear new empirical findings before it becomes mainstream enough to teach it in classes. Everyone recognizes that there's a lot of promise, though.
- dmagee 10y agograyclhn, you might find Rob Hyndman's article on cross validation helpful. http://robjhyndman.com/hyndsight/crossvalidation/ http://robjhyndman.com/hyndsight/crossvalidation/ " Asymptotically, minimizing the AIC is equivalent to minimizing the CV value. This is true for any model (Stone 1977), not just linear models. It is this property that makes the AIC so useful in model selection when the purpose is prediction."