Under what assumptions is the OLS estimator blue?

Under what assumptions is the OLS estimator blue?

Now for the implications. Under 1 – 6 (the classical linear model assumptions) OLS is BLUE (best linear unbiased estimator), best in the sense of lowest variance. It is also efficient amongst all linear estimators, as well as all estimators that uses some function of the x.

What is an error of assumption?

Model assumption errors occur with the use of methods, such as calibration, generalised regression estimator, calculation based on full scope or constant scope, benchmarking, seasonal adjustment and other models not included in the preceding accuracy components, in order to calculate statistics or indexes.

Which is a weaker assumption, OLs or assumptions 4?

Actually OLS is also consistent, under a weaker assumption than $(4)$ namely that: $(1)\\ E(u) = 0$ and $(2)\\ \\Cov(x_j , u) = 0$. The difference from assumptions 4 is that, under this assumption, you do not need to nail the functional relationship perfectly.

Do you need normality of errors assumption in OLS?

However, the normality-of-errors assumption is not required for the validity of the parameter estimates in OLS. For the details, the Wikipedia article on ordinary least squares regression lists four required assumptions; the normality of errors is listed as an optional fifth assumption.

What do you need to know about assumption 1?

Assumption 1: Linear Parameter and correct model specification. Assumption 1 requires that the dependent variable is a linear combination of the explanatory variables and the error terms . Additionally we need the model to be fully specified. A extensive discussion of Assumption 1 can be found here.

Which is not a strict assumption in OLS regression?

Expected value of error is still zero as it is assumed that the mean value of error clusters around zero. However the error need not be normally distributed which is not a strict assumption even in OLS regression.