Question: Consider the simple regression y i = x i + i where E[ | x] = 0 and E[ 2 | x] =
Consider the simple regression yi= βxi+ εiwhere E[ε | x] = 0 and E[ε2| x] = σ2
a. What is the minimum mean squared error linear estimator of β?
b. For the estimator in part a, show that ratio of the mean squared error of β to that of the ordinary least squares estimator b is
![MSE [B] MSE [b] where t2: (1+7?)' [o?/x'x]](https://dsd5zvtm8ll6.cloudfront.net/si.experts.images/questions/2023/04/643907cf96498_151643907cf78c6e.jpg)
τ is the square of the population analog to the ??t ratio?? for testing the hypothesis that β = 0, which is given in (5-11). How do you interpret the behavior of this ratio as τ ?????
MSE [B] MSE [b] where t2: (1+7?)' [o?/x'x]
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First c y c x c So E c x and Var 2 c c Therefore MSE 2 c x 1 2 s 2 c c To minimize this we set MSE c ... View full answer
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