How to do econometrics properly

2 Jun, 2023 at 13:26 | Posted in Statistics & Econometrics | 2 Comments

1. Always, but always, plot your data.
2. Remember that data quality is at least as important as data quantity.
3. Always ask yourself, “Do these results make economic/common sense”?
4. Check whether your “statistically significant” results are also “numerically/economically significant”.
5. Be sure that you know exactly what assumptions are used/needed to obtain the results relating to the properties of any estimator or test that you use.
6. Just because someone else has used a particular approach to analyse a problem that looks like yours, that doesn’t mean they were right!
7. “Test, test, test”! (David Hendry). But don’t forget that “pre-testing” raises some important issues of its own.
8. Don’t assume that the computer code that someone gives to you is relevant for your application, or that it even produces correct results.
9. Keep in mind that published results will represent only a fraction of the results that the author obtained, but is not publishing.
10. Don’t forget that “peer-reviewed” does NOT mean “correct results”, or even “best practices were followed”.

Dave Giles

Nowadays it has almost become a self-evident truism among economists that you cannot expect people to take your arguments seriously unless they are based on or backed up by advanced econometric modelling. So legions of mathematical-statistical theorems are proved — and heaps of fiction are being produced, masquerading as science. The rigour​ of the econometric modelling and the far-reaching assumptions they are built on is frequently not supported by data.

Modelling assumptions made in statistics and econometrics are more often than not made for mathematical tractability reasons, rather than verisimilitude. That is unfortunately also a reason why the methodological ‘rigour’ encountered when taking part of statistical and econometric research to a large degree is nothing but deceptive appearance. The models constructed may seem technically advanced and very ‘sophisticated,’ but that’s usually only because the problems here discussed have been swept under the carpet. Assuming that our data are generated by ‘coin flips’ in an imaginary ‘superpopulation’ only means that we get answers to questions that we are not asking.