(Requires Calculus)For the simple linear regression model of Chapter 4,

Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + u _ { i }

, the OLS estimator for the intercept was

\hat { \beta } _ { 0 } = \bar { Y } - \hat { \beta } _ { 1 } \bar { X }

, and

\hat { \beta } _ { 1 } = \frac { \sum _ { i = 1 } ^ { n } X _ { i } Y _ { i } - n \overline { X Y } } { \sum _ { i = 1 } ^ { n } X _ { i } ^ { 2 } - n \bar { X } ^ { 2 } }

Intuitively, the OLS estimators for the regression model

Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 i } + \beta _ { 2 } X _ { 2 i } + u _ { i }

might be

\hat { \beta } _ { 0 } = \bar { Y } - \hat { \beta } _ { 1 } \bar { X } _ { 1 } - \hat { \beta } _ { 2 } \bar { X } _ { 2 } , \hat { \beta } _ { 1 } = \frac { \sum _ { i = 1 } ^ { n } \bar { X } _ { 1i } Y _ { i } - n \bar { X } _ { 1 } \bar { Y } } { \sum _ { i = 1 } ^ { n } \bar { X } _ { 1 i } ^ { 2 } - n \bar { X } _ { 1 } ^ { 2 } }

and

\hat { \beta } _ { 2 } = \frac { \sum _ { i = 1 } ^ { n } \bar { X } _ { 2 i } Y _ { i } - n \bar { X } _ { 2 } \bar { Y } } { \sum _ { i = 1 } ^ { n } \bar { X } _ { 2 i } ^ { 2 } - n \bar { X } _ { 2 } ^ { 2 } }

By minimizing the prediction mistakes of the regression model with two explanatory variables, show that this cannot be the case.

Consider the Multiple Regression Model with Two Regressors X1 and X2

Multiple Choice