# Es will be biased downwards in the presence of

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Unformatted text preview: l B Consequences Inefﬁciency Consequence We want the estimators to have the smallest variance as possible (efﬁciency). Introduction Heteroscedasticity Past Exam Practice Question Moving Forward: Model B Consequences Inefﬁciency Consequence We want the estimators to have the smallest variance as possible (efﬁciency). OLS estimators are most efﬁcient under Gauss-Markov assumptions. Introduction Heteroscedasticity Past Exam Practice Question Moving Forward: Model B Consequences Inefﬁciency Consequence We want the estimators to have the smallest variance as possible (efﬁciency). OLS estimators are most efﬁcient under Gauss-Markov assumptions. If heteroscedasticity present, so A.4 is violated, then OLS estimators are inefﬁcient. Introduction Heteroscedasticity Past Exam Practice Question Moving Forward: Model B Consequences Inefﬁciency Consequence We want the estimators to have the smallest variance as possible (efﬁciency). OLS estimators are most efﬁcient under Gauss-Markov assumptions. If heteroscedasticity present, so A.4 is violated, then OLS estimators are inefﬁcient. 1 In that case, the estimators do not use information on heteroscedasticity. Introduction Heteroscedasticity Past Exam Practice Question Moving Forward: Model B Consequences Inefﬁciency Consequence We want the estimators to have the smallest variance as possible (efﬁciency). OLS estimators are most efﬁcient under Gauss-Markov assumptions. If heteroscedasticity present, so A.4 is violated, then OLS estimators are inefﬁcient. 1 2 In that case, the estimators do not use information on heteroscedasticity. Estimators need to take into account that observations with high disturbance variance are not as informative. Introduction Heteroscedasticity Past Exam Practice Question Moving Forward: Model B Consequences Inefﬁciency Consequence We want the estimators to have the smallest variance as possible (efﬁciency). OLS estimators are most efﬁcient under Gauss-Markov assumptions. If heteroscedasticity present, so A.4 is violated, then OLS estimators are inefﬁcient. 1 2 In that case, the estimators do not use information on heteroscedasticity. Estimators need to take into account that observations with high disturbance variance are not as informative. That is why, in principle, when heteroscedasticity present there are other estimators that have smaller variance than OLS estimators. Introduction Heteroscedasticity Past Exam Practice Question Consequences Wrong s.e. Consequence s.e. are estimators of s.d. of estimators. Moving Forward: Model B Introduction Heteroscedasticity Past Exam Practice Question Moving Forward: Model B Consequences Wrong s.e. Consequence s.e. are estimators of s.d. of estimators. They assume that distribution of disturbance term is homoscedastic (which reﬂects in s.d. of estimators). Introduction Heteroscedasticity Past Exam Practice Question Moving Forward: Model B Consequences Wrong s.e. Consequence s.e. are estimators of s.d. of estimators. The...
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## This document was uploaded on 03/12/2014 for the course ECON 202 at University of London University of London International Programmes (Distance Learning).

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