We start with the classical linear regression model and tests for restrictions of the parameter. We cover the geometric interpretation of OLS and elementary optimality theory (Gauss-Markov theorem), as well as GLS estimation and feasible GLS estimators. Then we deal with generalizations necessary for practical applications: Asymptotic theory, heteroscedasticity and consistent estimators for the variance of the estimation errors like Eicker-White.
Additionally, we discuss maximum-likelihood estimation, and the Wald-LM-LR tests and their equivalence.
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