1. We start from the model Y = Xß + u for which what we have called hypotheses are fulfilled ideals. Some authors, suspecting the presence of multicollinearity, have transformed the model by dividing each observation of all variables by the corresponding to one of them. a) Show that the random perturbations of the transformed model are heteroscedastic. b) Show that applying an efficient method to this reformulated model leads to some estimators that coincide with the OLS applied to the original model c) Show that the ÕLS applied to the original model are more efficient than the OLS applied to the reformulated model.

MATLAB: An Introduction with Applications
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1. We start from the model Y = Xß + u for which
what we have called hypotheses are fulfilled
ideals. Some authors, suspecting the presence of
multicollinearity, have
transformed the model by dividing each
observation of all variables by the
corresponding to one of them.
a) Show that the random perturbations of the
transformed model are
heteroscedastic.
b) Show that applying an efficient method to this
reformulated model leads to
some estimators that coincide with the OLS
applied to the original model
c) Show that the ÕLS applied to the original model
are more efficient than the OLS
applied to the reformulated model.
Transcribed Image Text:1. We start from the model Y = Xß + u for which what we have called hypotheses are fulfilled ideals. Some authors, suspecting the presence of multicollinearity, have transformed the model by dividing each observation of all variables by the corresponding to one of them. a) Show that the random perturbations of the transformed model are heteroscedastic. b) Show that applying an efficient method to this reformulated model leads to some estimators that coincide with the OLS applied to the original model c) Show that the ÕLS applied to the original model are more efficient than the OLS applied to the reformulated model.
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