Calculating robust errors versus-conducting weighted squares

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Suppose you detect heteroskedasicity and /or auto correlated errors in your regression. What is the difference between (I) calculating robust errors versus (ii) conducting a weighted least squares or feasible generalized least squares analysis. What are the pros and cons of each approach? Give an example of when each might be preferred over the other.

Reference no: EM13834853

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