No "must do" list is ever going to be complete, let alone perfect. This is certainly true when it comes to itemizing essential ground-rules for all of us when we embark on applying our knowledge of econometrics.
That said, here's a list of ten things that I like my students to keep in mind:
- Always, but always, plot your data.
- Remember that data quality is at least as important as data quantity.
- Always ask yourself, "Do these results make economic/common sense"?
- Check whether your "statistically significant" results are also "numerically/economically significant".
- 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.
- Just because someone else has used a particular approach to analyse a problem that looks like yours, that doesn't mean they were right!
- "Test, test, test"! (David Hendry). But don't forget that "pre-testing" raises some important issues of its own.
- Don't assume that the computer code that someone gives to you is relevant for your application, or that it even produces correct results.
- Keep in mind that published results will represent only a fraction of the results that the author obtained, but is not publishing.
- Don't forget that "peer-reviewed" does NOT mean "correct results", or even "best practices were followed".
I'm sure you can suggest how this list can be extended!
© 2013, David E. Giles