Hi All: If one has an AR type regression model so that there's a lagged
independent variable, then testing for autocorrelated residuals is
correctly  done ( learned this recently ) using the Breusch-Godfrey test (
bgtest in lmtest in R. thank you Achim ) .

But, now suppose that the BG test rejects the null of no autocorrelation and
consequently one then builds a model that tries to deal with the
autocorrelated residuals  ( gls or optim whatever ). Now that person wants
to test if the "new model" still has autocorrelated residuals or
did the "new model" "fix" the problem.  So, my 64K question is , is it
better to then go back and use Box-Leung or Portmenteau or Durbin or
whatever on the latest residuals  or should I still be using the bgtest ? Or
maybe the answer is that it's all just useless because every model is a very
crude approximation to reality anyway. Thanks for any insights.


Mark

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