Multicollinearity depicts the stage in which the independent variables in the data show high interactions and associations. Therefore, multicollinearity is considered a disturbance that causes volatility in data.
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There are reasons behind the outcome of multicollinearity in data.
Multicollinearity can occur due to the improper utilization of dummy variables. Researchers who are not experts can end up causing multicollinearity in the data.
If the researcher includes a variable which is being computed from the other variables in the equation, then this action can cause multicollinearity in the data. For example, if the family’s health is equal to the husband’s health+ wife’s health + child’s health, and the regression includes all four health cases, then this calls for multicollinearity.
If a researcher includes the same type of variables twice in an experiment, then this activity performed by the researcher causes multicollinearity. For example, if two models of Nokia phones are included as different variables in the study, then this causes multicollinearity in the data.
There are certain outcomes of multicollinearity.
The researcher should note that as the level of multicollinearity increases, the value of the standard error gets higher and higher. When there is high multicollinearity in the data, then the confidence intervals for the coefficients tend to be extremely wide and the value of the t-statistics tends to be very small. The researcher should keep in mind that the value of the coefficients should be larger in order to have it statistically significant. In other words, in the presence of multicollinearity, the null hypothesis assumed by the researcher is harder to get rejected.
If the value of the tolerance is closer to the value of one, then this means that there is very little multicollinearity. On the other hand, if the value of the tolerance is closer to zero then this means that there is very high multicollinearity. So, in the latter case, the multicollinearity is considered a threat.
The reciprocal of the tolerance is known as the variance inflation factor (VIF). The variance inflation factor shows the amount of variance of the coefficient estimate that is inflated by multicollinearity.
Multicollinearity is not something that is discreet in nature, but is a matter of degree. The matter of multicollinearity can be detected with the help of certain warning signals.
If the t ratios for each coefficient are statistically significant, and the F statistic is not statistically significant, then this indicates that there is multicollinearity in the data.
It is important to check the stability of the coefficients when two different types of samples are used. If the coefficients differ quiet significantly, then this shows that there is multicollinearity in the data.
If the sign of the variables gets changed, or if some variables are being added, then this signifies the presence of multicollinearity.
In order to address the problem of multicollinearity, one has to make sure that he/she does not conduct improper usage of the dummy variables.
If the sample size is increased from the desired sample size, then this will decrease the value of the standard errors and simultaneously it would decrease the level of multicollinearity.
It is sometimes suggested that the researcher drop the variable that is causing multicollinearity. The researcher should keep in mind that if the most important variable is dropped by the researcher, then this would cause a specification error, which is even worse than multicollinearity.
The most important thing for obtaining a valid inference about the data is to realize the presence of multicollinearity. Additionally, a researcher should be aware of the consequences of multicollinearity.
