Detection of multicollinearity Assignment Help

Assignment Help: >> Multicollinearity - Detection of multicollinearity

Detection of multicollinearity:

Note that multicollinearity is almost always present in most applications, so it is a matter of degtee and not whether it is  present or not. What most often we are  trying to find out  in empirical applications  is whether  it is  likely to create problems  in terms of hypothesis testing or not (through a higher estimate of standard errors), since the objective  of most empirical studies  is usually  to find  and  test the exact  relationship between variables. There does  not exist  any  formal  tests for multicollinearity, however there does exist some informal rules of thumb that most researchers use for this purpose. These are as follows:  

High R2 but few significant t-statistics. This is the "classic"  symptom of multicollinearity.  If  the R2  is high, say in excess of 9.8,  the F  test in most:cases. will reject the hypothesis  that  estimated coefficient$  are all simultaneously  zero. The individual  t tests, however, will show that none or very few of  the slopes (coefficients)  estimates  are statistically different  from zero. This was illustrated earlier in the example shown above.

High pair-wise correlations among regressors. Another suggested rule of thumb is  that if  the pair-wise correlation  coefficient  between two regressors is high, say, in excess of 0.8, then multicollinearity is a serious problem. The problem with  this criterion  is  that, this is a sufficient condition  for  multicollinearity but not a necessary condition. In other words, there can be problems of multicollinearity  present even when this condition  is  not fulfilled.

 

Auxiliary regressions
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