Question 3 (8 points; ~12 minutes). Omitting a relevant explanatory variable fro

Question

Question 3 (8 points; ~12 minutes). Omitting a relevant explanatory variable from a regression model, when that variable is correlated with some of the other explanatory variables, typically will result in the OLS estimators for all the estimated effects to be biased and in consistent. Will the inclusion of an irrelevant independent variable, correlated with some of the other independent variables but uncorrelated with the error term, result in biased and inconsistent OLS estimators? Explain. Answer 3:

Explanation / Answer

Answer:

1) Inclusion of an irrelevant independent varaible will cause the problem of "omitted variable biased".

2) If this irrelevant independent variable is corelated with other independent variable, then the problem of "multicollinearity" will occur. Multicollineraity is phenomena that occurs among independent variable when it is highly corelated among themselves. Due to existence of this property the variance of the model (SEE) and the varaince of the coeffcients will be very high. Not only this the coefficients of the linear model may have high standard error but low significance leve inspite of having high R2. Hence making the model inconsistent.

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