{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Lecture11 - Lecture 11 Multicollinearity Econ 444 Winter...

Info iconThis preview shows pages 1–7. Sign up to view the full content.

View Full Document Right Arrow Icon
Lecture 11 : Multicollinearity Econ 444, Winter 2010 Feb 22, 2010
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Fig 1. No Correlation between independent variables X1 and X2 – very rare as most economic variables are correlated with each other.
Background image of page 2
Fig 2 : Strong Correlation between independent variables X1 and X2 – case of imperfect multicollinearity.
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Fig 3. Perfect Multicollinearity-X2 is a perfect function of X1. Therefore, including X2 would be irrelevant because it does not explain any of the variation on Y that is not already accounted by X1.
Background image of page 4
Perfect Multicollinearity : imply perfect correlation between independent variables in a regression equation. Hence if we want to estimate, Y i = β 0 + β 1 X 1 i + β 2 X 2 i + i (1) then, above regression is a case of perfect multicollinearity if correlation between X 1 i and X 2 i is 1 : Perfect Multicollinearity ⇔ | Corr ( X 1 i , X 2 i ) | = 1 Perfect multicollinearity is a violation of Classical Assumption VI. In this case, OLS estimator does not exist.
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
To illustrate the problem consider the following demand function for food, Y i = β 0
Background image of page 6
Image of page 7
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}