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Major Implications:

The important  implications  of multicollineam'ty  are given below. In  the  presence of high, though not perfect, multicollinearity:

1)  The OLS coefficient  estimates have large variances.

2)  Because of large variance  the confidence  intervals will be very large, which in turn  means that there is a high probability of accepting the null hypothesis of zero coefficient,  even when the actual parameter is positive.

3)  Overall  the  regression may do very well, i.e., the R may be quite high despite not being able to reject the hypothesis of one or more parameters being equal to zero.

4)  The OLS estimates and their standard errors can be verysensitive  to small changes  in the data.


Note that fiom  a  practical standpoint  this  is  an extremely serious  problem since  most empirical studies  try to estimate  the impact of one or more economic variables on a particular variable, such as  the  income consumption relationship discussed above.
So if income and wealth are highly correlated  then although  the regression gives the true coefficient estimates, we cannot reject the null hypothesis that the impact of  income on consumption expenditure  is zero. This means  that  the whole purpose of  the study  islost, as nothing can be conclusively (statistically speaking)  proved.

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