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Unformatted text preview: We found that the variance of our ols estimators is: Var j = 2 [ i = 1 n X ij X j 2 1 R j 2 ] because we do not know the u i , we cannot calculate 2 = n 1 i = 1 n u i 2 An unbiased estimator of 2 is 2 = n k 1 1 i = 1 n u i 2 In the case of k+1 parameters, df=nk1 3. If the errors are homoskedastic, then an estimate of the var( ) using 2 is unbiased Var j = 2 [ i = 1 n X ij X j 2 1 R j 2 ] ^ ^ 4. Gauss Markov Theorem The OLS estimator is the Best Linear Unbiased Estimator (BLUE) Linear linear in parameters Unbiased E[ ]= Best smallest variance Under assumptions 14 and homoskedasticity, is the BLUE of . These are known as the GaussMarkov Assumptions ^ ~ ^ Interpreting OLS Paramter Estimates Recall, in our regression Y i = + 1 X 1i + 2 X 2i +...+ k X ki + u i i=1,...,n 1 is the partial effect of X 1 on Y....
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This note was uploaded on 01/26/2012 for the course ECON 401 taught by Professor Burbidge,john during the Fall '08 term at Waterloo.
 Fall '08
 Burbidge,John

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