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According to Koutsoyiannis (2010), when any two explanatory (independent)

variables are changing in nearly the same way, it becomes extremely difficult to

establish the influence of each one regressor on Y separately. Therefore the term

multicollinearity is used to denote the presence of linear relationships (or near linear

relationships) among explanatory variables. If the explanatory variables are perfectly

linearly correlated, that is, if the correlation coefficient for these variables is equal to

unity, the parameters become indeterminate; it is impossible to obtain numerical

values for each parameter separately and the method of least squares breaks down.

Multicollinearity is a state of very high inter-associations among the independent

variables. It occurs when two or more independent variables have high correlation

coefficients amongst themselves. This situation creates redundant information,

skewing the results in the regression model. It is a type of disturbance in the data the

statistical inferences made about the data may not be reliable. It occurs when

variables are highly correlated to each other and when there is repetition of the same

variable. Multicollinearity makes it difficult to gauge the effect of independent

variables on dependent variables. According to Gensa, (2016), and Stephenie

(2017), causes of multicollinearity include; insufficient data, variables in the

regression that is actually a combination of two other variables, and two identical (or

It was tested using a Variance Inflation Factor (VIF) and other tests. As said above

multicollinearity is the tendency of independent variables in the regression analysis

to have close relationship or interrelated as such make the coefficients estmates

indeterminate. It should be clearly understood that in testing for multicollinearity the

test is dealing with the extent of interrelation among independent variables and not

absence or presence of multicollinearity, since multicollinearity is there in every

regression analysis. What appears to be critical in this testing is to determine the

lowest level, of interrelationship among the independent variables, since every

technique employed in determining multicollinearity provides or gives a certain level

of interrelationship among independent variables.

The frequently employed techniques include Variance Inflation Factor (VIF),

Pairwise correlation and Tolerance (Gujarat, 2009). The consequences of estimating

the equation while there are high multicollinearity among independent variables

include: Even when the ordinary square (OLS) are best linear unbiased estimator

(BLUE) but its estimates are not precise since the estimates are not precise then the

confidence intervals become more wider as such very often may lead to acceptance

of null hypothesis. Apart from widening the confidence interval but also r ratio

statistics becomes statistically insignificant and R2 becomes extremely high (Gujarat, 2009).

Usually multicollinearity is detected by simply looking at the high R2 but few significant t ratios. For instance when R2 or adjusted R2 is high, exceeding 0.8 while F statistic after rejecting H0 and at the same time t ratios showing very few or no

among the independent variables. However, Gujarat (2009) points out that,

multicollinearity test is exceptionally strong but is only regarded bad, when any two

explanatory variables are changing in nearly the same way, such that, it becomes

extremely difficulty to establish the influence of each one regressor on Y separately.

Hence apart from using coefficients of determination also the study dealt with

multicollinearity using both pair-wise correlation test and tolerance and Variance

Inflation Factors (VIF).

3.13.1.Pair wise Correlation Test

Usually this test is employed to determine the correlation of independent variables if

it is too high or too low. This test suggests that the pair-wise or zero-order

correlation coefficient between two independent variables is high, if it exceeds 0.8,

thus multicollinearity becomes a problem and if it is less there is no problem of

multicollinearity among independent variables. Gujarat (2009) points out that high

zero order correlations are a sufficient but not a necessary condition for the existence

of multicollinearity because it can exist even when the zero order or simple

correlations are comparatively low, (less than 0.5). Hence it important to remember

that the effect of multicollinearity should not be judged only by looking at the high

zero order correlation but also at its ability to explain the dependent variable.

Therefore this study neglects or disregards this test and opts for variance inflation

factor (VIF).

3.13.2.Variance Inflation Factor

Variance Inflation Factor (VIF) and Tolerance (Tol) are the widely employed

the degree of correlation among independent variables as such it becomes easy to

identify which variables are more collinear. The closer tolerance to 0 (zero) implies

that there are problems of co linearity among independent variables; whereas closer

tolerance to 1(one) implies that independent variables are not collinear to each other.

Again VIF detects multicollinearity, VIF values exceeding 10 signifies that

variables are collinear but if or when less than 10 then there is no problem of

multicollinearity among the independent variables (Gujarat, 2009).

In addition, proper solution for multicollinearity is still questionable or disputable.

So far there are two possible answers or options. 1st do nothing and 2nd do something school of thought. Generally speaking ‘do nothing school of thought’ under Blanchard argues that: when students run their first ordinary least squares

(OLS) regression, the first problem they usually confront is that of multicollinearity.

Consistent with Blanchard school of thought multicollinearity is in essentially a data

deficiency problem (micronumerousity) and sometimes we have no alternative over

the availability of data for empirical analysis. As stated by Gujarat (2009) it is not

that all the coefficients in regression model are statistically insignificant.

Furthermore, even if we cannot estimate one or more regression coefficients with

greater accuracy or precision a linear combination of them can be relatively

estimated efficiently. On the other hand, other schools of thoughts propose to do

something regarding multicollinearity problems such as combining cross sectional

and time series data to form pooled data or dropping some variables. However

dropping some variables causes problems of specification bias or specification error.

dividing the variables with common root. Besides the study may add new data (new

sample).

Alternatively checking for co linearity can be done using the Durbin-Watson

statistics. Normally Durbin-Watson statistic value lies between 0 and 4. A Value

close to 2 suggests no problem of correlation whereas value close to 0 implies

‘negative correlations. Values close to 4 implies positive correlations (Ayer, 2008). Notwithstanding all these techniques for answers are not devoid of weaknesses. This

study relied much on Tolerance and Variance Inflation Factor (VIF) in determining

multicollinearity due its reliability and its simple decision criteria.