Definition of 'Model Diagnostics' Can anyone help me out with explaining what the term 'model diagnostics' refers to when applied to multiple regression please?
In particular, what tests are necessary to check whether your estimated model is congruent?
Thanks,
L
 A: After a regression model is fitted, it is necessary to check that the assumptions of the classical regression model are met, otherwise estimates based on the Ordinary Least Squares estimator may be biased or inefficient and inference based on OLS may be unreliable.
Some of the assumptions of the classical regression model rely on the structure of the disturbance term. Although the disturbances (shocks or errors) are not observed, they can be estimated as the residuals of the fitted model. Upon the residuals, we should test whether they are independent and homoscedastic. For that, the following tests are common:


*

*Breusch and Godfrey test for the presence of serial correlation.

*Durbin and Watson test for first order autocorrelation in the error term. This test is also often regarded as a general test of misspecification of the model.

*Breusch and Pagan test for homogeneity of the variance of the error term.


If autocorrelation or heteroscedasticity is detected, then we have to consider 
choosing other estimator, e.g. Generalized Least Squares, in order to improve the efficiency of the estimates.
When the explanatory variables are not fixed, it should be checked whether 
they are uncorrelated with the disturbance term. The Durbin–Wu–Hausman test can be used to that end. If the null of uncorrelation between the explanatory variables and the disturbances is rejected, then the instrumental variables estimator has better properties than the standard OLS in large samples.
A test for the stability of the coefficients across the sample can be carried out for example by means of the Chow test.
The validity of the linear relationship among the variables against a non-linear relationship can be tested by means of the Ramsey test.
