By Patrick Kent Watson, Sonja Sabita Teelucksingh
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Additional resources for A Practical Introduction to Econometric Methods Classical and Modern
E. 71113 Sum squared resid 6517534. 83 on imports). Furthermore, the R2 and F-statistics leave us in no doubt that this is a good fit. What then do we do? It is very difficult to answer this question at this stage of our study of econometrics but at least one word of warning is in order: the conclusions about the significance of this or that variable under the conditions of multicollinearity shown here are very tenuous and great caution should be exercised in using any of the results shown. Furthermore, the process through which variables are (arbitrarily) added and dropped from an equation, as was done here, has given rise to charges of data mining.
If the multicollinearity is very great, this is an intuitively justifiable procedure – the same information will still to some extent be available from the remaining variables. This amounts, however, to setting the true coefficient values of the omitted variables equal to zero, and unless this is effectively true, the OLS parameter estimates will be biased, as we shall see below. MULTICOLLINEARITY: AN ILLUSTRATION Let us once again consider the import function that we have looked at in chapters 1 and 2.
Any off-diagonal elements close to one would therefore indicate the presence of severe multicollinearity in a model. This is not to say, however, that off-diagonal values not close to unity imply the absence of multicollinearity. For herein lies the danger of this method as an indicator of multicollinearity – while there may not be a strong correlation between any two variables in the model as indicated by this matrix, there could exist a strong correlation between combinations of the explanatory variables, which this matrix cannot indicate.
A Practical Introduction to Econometric Methods Classical and Modern by Patrick Kent Watson, Sonja Sabita Teelucksingh