12, The higher value M ayye lower value of k b), the higher valve of F tess c) t
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12, The higher value M ayye lower value of k b), the higher valve of F tess c) the larger is variance of p d), all of the ahove. e) none of the above 13, If Y is salary and X is experience then the appropriate functivaal Sverm is 14.Which one of the following is NOT a plausible remedy for sot severe multicollinearity: a) Use principle components analysis b) Drop one of the collinear variables c) Use a longer run of data d) Take logarithms of each of the variables What will be the properties of the OLS estimator in the presence of multicollinearity a) It will be consistent, unbiased and efficient b) It will be consistent, unbiased and not efficient c) It will be consistent but not unbiased d) It will not be consistent 16.Which of the following statement is correct? a) In an econometric model we must first realize that economic relations are not exact. b) Economic theory describes the average or systematic behavior of many individuals c) Econometric theory describes the average or systematic behavior of many d) Every economics model is comprised of a systematic portion and an unobservable e) All of the above statements are correct. or firms. individuals or firms. random component. 17.Which of the following statements is correct ? a) The model Y = ? + ßX3 + ut is called a simple linear regressionExplanation / Answer
14) Option D.:- Take algorithms of each of the variables.
Explanation:
Principal components analysis (PCA) is a plausible response to a finding of multicollinearity. This technique works by transforming the original explanatory variables into a new set of explanatory variables that are constructed to be orthogonal to one another. The regression is then one of y on a constant and the new explanatory variables. Another possible approach would be to drop one of the collinear variables, which will clearly solve the multicollinearity problem, although there may be other objections to doing this. Another way is to involve using a longer run of data. Such an approach would involve increasing the size of the sample, which would imply more information upon which to base the parameter estimates, and therefore a reduction in the coefficient standard errors, thus counteracting the effect of the multicollinearity. Finally, taking logarithms of the variables will not remove any multicollinearity.
15)Correct Answer :Option A) It will be consistent,unbiased and efficient.
Explanation:- In the presence of multicollinearity, the OLS estimator will still be consistent, unbiased and efficient. This is the case since none of the four (Gauss-Markov) assumptions of the CLRM have been violated. You may have thought that, since the standard errors are usually wide in the presence of multicollinearity, the OLS estimator must be inefficient. But this is not true - the multicollinearity will simply mean that it is hard to obtain small standard errors due to insufficient separate information between the collinear variables, not that the standard errors are wrong.
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