1. Assumption MLR.3 Notes (No Perfect Collinearity)
Perfect Collinearity can exist if:
One variable is a constant multiple of another
Logs are used inappropriately
One variable is a linear function of two or more other variables
In general, all of these issues are easy to fix, once they are identified.

2. Assumption MLR.3 Notes (No Perfect Collinearity)
One variable is a constant multiple of another
-ie: Assume that Joe only drinks coffee at work, and drinks exactly 3 cups of coffee every day he works. Therefore:
-including both coffee and work in the regression would cause perfect collinearity; the regression would fail

3. Assumption MLR.3 Notes (No Perfect Collinearity)
2) Logs are used inappropriately
-Consider the following equation and apply log rules:
-a variable is included twice, causing an inability to estimate B1 and B2 separately
-note that geek and geek2 could both have been used, as they are not linearly related

4. Assumption MLR.3 Notes (No Perfect Collinearity)
3) One variable is a linear function of two or more other variables
-Consider a teenager who spends all their income on movies and clothes:
-if income and expenditures on both movies and clothes are in the regression, perfect collinearity exists and the regression fails

5. 3.3 Fixing Multicollinearity
Drop a variable from the model.
If one variable is a multiple of another, it adds nothing to consider it twice
Ditto for logs
If the elements of a sum are in a regression, the sum itself is redundant. (Alternately one of the elements can be omitted).

6. 3.3 Multicollinearity and N
-Assumption MLR.3 can also fail is N is too small
-in general, MLR.3 will always fail if n<k+1 (the number of parameters)
-even if n>k+1, MLR.3 may fail due to a bad sample
Next we have the most important assumption for proving OLS’s unbiasedness:

7. Assumption MLR.4 (Zero Conditional Mean)
The error u has an expected value of zero given any values of the independent variables. In other words,

8. Assumption MLR.4 Notes (Zero Conditional Mean)
MLR.4 fails if the functional relationship is misspecified:
A variable is not included the correct way
-ie: consumption is included in the regression but not consumption2 and the true relationship is quadratic
2) A variable is included the incorrect way
-ie: log(consumption) is included in the regression but consumption is the true relationship
-In these cases, the estimators are biased

9. Assumption MLR.4 Notes (Zero Conditional Mean)
MLR.4 also fails if one omits an important factor correlated with any x
-this can be due to ignorance or data restrictions
MLR.4 also fails due to:
Measurement error (ch. 15)
2) An independent variable is jointly determined with y (ch. 16)

10. Assumption MLR.4 Notes (Zero Conditional Mean)
When MLR.4 holds, we have EXOGENOUS EXPLANATORY VARIABLES
When MLR.4 does not hold (xj is correlated with u), xj is an ENDOGENOUS EXPLANATORY VARIABLE

11. MLR.3 vs. MLR.4 (Cage Match)
MRL.3 deals with relationships among independent variables
-if it fails, OLS cannot run
MLR.4 deals with relationships between u and independent variables
-it is easier to miss
-it is more important

12. Theorem 3.1 (Unbiasedness of OLS)
Under assumptions MLR.1 through MLR.4,
For any values of the population parameter Bj. In other words, the OLS estimators are unbiased estimators of the population parameters.

13. 3.3 Is OLS valid? -MLR.3 is satisfied -MLR.2 is satisfied
IF OLS runs (B estimates are found)
-MLR.3 is satisfied
IF the sample is random
-MLR.2 is satisfied
IF we have some reason to suspect a true relationship
-MLR.1 is valid
Therefore if we believe MLR.4 holds true
-OLS is valid!

14. 3.3 What is unbiasedness?
Our estimates of Bhat are all numbers
-numbers are fixed, and cannot be biased or unbiased
MLR.1 through MLR.4 comments on the OLS PROCEEDURE
-Is our assumptions hold true, our OLS PROCEEDURE is unbiased.
In other words:
“we have no reason to believe our estimate is more likely to be too big or more likely to be too small.”

15. 3.3 Irrelevant Variables in a Regression Model
Including independent variables that do not actually affect y (irrelevant variables) is also called OVERSPECIFYING THE MODEL
-Consider the model:
-where x3 has no impact on y; B3=0
-x3 may or may not be correlated with x2 and x1
-in terms of expectations:

16. 3.3 Irrelevant Variables in a Regression Model
From theorem 3.1, B1hat and B2hat are unbiased since MLR.1 to MLR.4 still hold
We furthermore expect that:
-even though B3hat may not be zero, it will average out to zero across samples
-Including irrelevant variables doesn’t affect OLS unbiasedness, but we will see it affect OLS variance