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For Classical Regression Model the OLS or Ordinary Least Squares - estimators (or the betas) are BLUE (Best, Linear, Unbiased, Estimator) when :

  1. The regression is linear in the coefficients, it is correctly specified and has an additive error term.
  2. Mean of the error term is zero. (Include a constant term in the regression (B0 which will force the mean to be zero)
  3. The independent variables are not correlated with the error term. (If they are correlated then the betas will be biased.)
  4. Observations of the error term (the residuals) are not correlated with each other.
  5. The error term has a constant variance (Homoskedasticity)
  6. No independent variable is a perfect linear function of any of the other independent variable. (If this is true - multicollinearity will occur)
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Q: When are OLS estimators BLUE?
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