OHCh4-B - chapter 4: The Classical Model Lecture 2 1...

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chapter 4: The Classical Model Lecture 2
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1 Variance of the OLS Estimators Under the Classical Assumptions (except for VII), the variance of the OLS estimators can be expressed in terms of the unknown variance of the error term, V AR ( i ), which we usually denote by ± 2 . For the regression with V AR ( ^ ² 1 ) = ± 2 P N i =1 ( X i ± X ) 2 (1) For the purpose of interpreting this, it is better to multiply and divide the denominator by N , so that we have the sample size multiplied by the sample variance of X in the denominator.
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( ^ 1 ) = ± 2 N [ P N i =1 ( X i X ) 2 ) =N ] (2) If ± 2 = V AR ( ² i ) increases, then V AR ( ^ 1 ) increases. If the sample size N increases, then V AR ( ^ 1 ) decreases. If the sample variance of X increases, then V AR ( ^ 1 ) decreases. Do these statements make sense? why? 2
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OHCh4-B - chapter 4: The Classical Model Lecture 2 1...

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