Regression - Expected Values and Variances of the OLS estimators
4 important questions on Regression - Expected Values and Variances of the OLS estimators
What four assumptions (SLR1-SLR4) are needed for unbiasedness of OLS?
SLR2 (Random sampling): We have random sample {(x(i),y(i));(i)=1,..,n} following SLR1's population model
SLR3 (Sample variation in the explanatory variable): The sample values x1,..,xn of the explanatory variable are not all the same
SLR4 (Zero conditional mean): E(u|x)=0
SLR1-SLR4 gives us E(^B0)=B0 and E(^B1)=B1
What additional assumption is needed for deriving variances of OLS estimators?
SLR5 (Homoskedasticity): var(u|x) = o2
SLR4 and SLR5 are equivalent to E[y|x]=B0+B1x and var(y|x)=o2
What is the sampling variance under SLR1-SLR5?
- Higher grades + faster learning
- Never study anything twice
- 100% sure, 100% understanding
How can the error variance and the standard error be estimated?
The question on the page originate from the summary of the following study material:
- A unique study and practice tool
- Never study anything twice again
- Get the grades you hope for
- 100% sure, 100% understanding
![Trustpilot Logo](/images/design_2020/lp/clp-trustpilot-en.png)