The sign of the bias of when omitting X in the estimation of Y Bo BiX B2X2 u can be summarized in the table below B 0 B 0 Corr (X1,X2) 0 Corr (X1,X) 0 This exercise will help you work through the cases above by illustrating them with examples of your own For two cases, follow the steps below Assume the regression equation you would like to estimate is Y Bo BX BX u, but for some reason (lack of data or knowledge, for example), you end up omitting X from the regression Feel free to draw on examples from class but do not copy directly 1 Describe the hypothetical variables Y, X, and X 2 Indicate the sign of the correlation between X and X, and explain why you would expect such a sign 3 Indicate the sign of P2, and explain why you would expect such a sign 4 Indicate the sign of the bias if you were to omit X from the regression 5 Indicate how your estimate of is likely to change when omitting X from the regression, i e will it get larger or smaller relative to when you estimate the full regression (with both X and X as explanatory variables) Explain your reasoning both in technical terms (using your answers to the previous sub questions) and in terms a policymaker can understand

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The sign of the bias of when omitting X in the estimation of Y = Bo + BiX + B2X2 +u can be summarized

$The sign of the bias of ( beta_{1} ) when omitting ( X_{2} ) in the estimation of [ Y=beta_{0}+beta_{1} X_{1}+beta_{$

The sign of the bias of when omitting X in the estimation of Y = Bo + BiX + B2X2 +u can be summarized in the table below. B>0 B 0 + Corr (X1,X)

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