1. ECONOMETRICS ADVANCED SAJID ALI KHAN
GREEN HILLS POSTGRADUATE COLLEGE RAWALAKOT POONCH AZAD KASHMIR

2. TOPIC
MULTICOLLINEARITY
Collinearity
Multicollinearity
Sources of Multicollinearity
Types of Multicollinearity

3. COLLINEARITY
In a multiple regression model with two independent variables, if there is linear relationship between independent variables, we say that there is collinearity. .

4. MULTICOLLINEARITY
If there are more than two independent variables and they are linearly related, this linear relationship is called multicollinearity.

5. Multicollinearity arises from the presence of interdependence among the regressors in a multivariable equation system. The departure of orthognality in the set of regressors in a measure of multicollinearity.

6. It means the existence of a perfect or exact linear relationship among some or all explanatory variables. When the explanatory variables are perfectly correlated, the method of least squares breaks down.

7. SOURCES OF MULTICOLLINEARITY
The data collection method employed for example, sampling over a limited range of the values taken by the regressors in the population.
Constraints on the model or in the population being sampled. In the regression of electricity consumption (Y) on income ( X1) and house size (X2 ) there is a physical constraints in the population in that families with higher income generally larger homes than families with lower income.

8. Model specification: For example adding polynomial terms to a regression model, especially when the range of the variable is small.
An Over determined Model: This happens when the model has more explanatory variables than the number of observations. This could happen in medical research, where there may be a small number of patients about whom information is collected on a large number of variables. . ̂

9. An additional reason for multicollinearity, especially in time series data may be that the regressors included in the model share a common trend, that is they all increase or decrease over time. Thus in the regression of consumption expenditure on income, wealth and population, the regressors income, wealth and population may all be growing over time at more or less the same rate leading to collinearity among these variables.

10. TYPES OF MULTICOLLINEARITY
Perfect Multicollinearity
Relates to the situation where explanatory variables are perfectly linearly related with each other. Simply when correlation between two explanatory variables is exactly one. This situation is called perfect multicollinearity.
Imperfect Multicollinearity
If the correlation coefficient between two explanatory variables is not equal to one but close to one approximately 0.9, it is called high multicollinearity. If approximately 0.5, it is called moderate and if it is called low multicollinearity. Both are troublesome because it cannot be easily detected.
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