Imagine we have already built our linear regression model, with a certain dataset.

Which order of tests would you follow to be sure that whatever conclusions you may want to extract are correct. For example:

  1. In the beginning we may want to test for non-linearities not present in our model, but present in reality (model misspecification). For this objective, we could use RESET. What others can we use and why?
  2. Now we could want to test for heteroskedasticity(Breusch-Pagan and White's tests
  3. Endogeneity (Durbin-Wu-Hausman test)
  4. ...

Which 'game plan' do you use and why?

  • $\begingroup$ The check list should include careful thought about endogineity issues, I think. $\endgroup$ – 123 Jun 1 '16 at 13:06

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