Consider the regression model: y = XB, +u. Assume that all the above standard assumptions i)...

 

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Consider the regression model: y = X₁B, +u. Assume that all the above standard assumptions i) iv) are satisfied. Suppose that you estimated a wrong model: y=X,B₁ +X₂B₂ +e. In other words, you erroneously included X2. Let us define the OLS estimator of B, in the wrong model as ₁. For y, X, and X₂, the following data are given. y= X₁ 10 X₂ Show that var(ß, -₁)= var(,) - var(ß,). Note that, when random variables A and B are (mx1) column vectors, cov(A,B) = E[{A-E(A)} {B-E(B)}]. [10] Consider the regression model: y = X₁B, +u. Assume that all the above standard assumptions i) iv) are satisfied. Suppose that you estimated a wrong model: y=X,B₁ +X₂B₂ +e. In other words, you erroneously included X2. Let us define the OLS estimator of B, in the wrong model as ₁. For y, X, and X₂, the following data are given. y= X₁ 10 X₂ Show that var(ß, -₁)= var(,) - var(ß,). Note that, when random variables A and B are (mx1) column vectors, cov(A,B) = E[{A-E(A)} {B-E(B)}]. [10]

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The mixed variable ZAXBY while XYN01 and AB can get 1 or 1 in the same probability 05 is normal dist... View the full answer