INTRODUCTION TO ECONOMETRICS II ECO 306 NOUN 82 Therefore,
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Which is unusual for thereto bean exact relationship among the explanatory variables in a regression. So,
when this occurs, it is typical because there is a logical error in the specification.
2.3.4.1 Multicollinearity in Models with More Than Two Explanatory Variables The previous discussion of multicollinearitywas restricted to the case where there are two explanatory variables. In models with a greater number
of explanatory variables, multicollinearity maybe caused by an approximately linear relationship among them. It maybe difficult to discriminate between the effects of one variable and those of a linear combination of the remainder. In the model with two explanatory variables, an approximately linear relationship automatically
means a high correlation, but when there are three or more, this is not necessarily the case. A linear relationship does not inevitably imply high pairwise correlations between any of the variables. The effects of multicollinearity are the same as in the case with two explanatory
variables and as in that case, the problem may not be serious if the population variance of the disturbance term is small, the number of observations large and the variances of the explanatory variables are equally large.
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