Specification Bias Explanation (1)

Specification Bias Explanation (1) - Specification Bias a...

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Specification Bias a. Specification mistake - suppose an important variable, X 2 , is left out of the regression model. The true model is: 0 1 1 22 i i ii Y X Xu b bb = +++ But, you assume: 0 11 i Y Xv aa = ++ (What CRM assumptions have been violated? Assumption #1 and Assumption #3.) b. What happens - Verbally. Your model assumes only X 1 causes Y to change, but in truth, the variable X 2 also causes Y to change. The effects of X 2 on Y are not accounted for in your model. As a result, the effect of X 2 Y gets tangled up with the effect of X 1 Y . We can't get a clear picture of how changes in X 1 affect changes in Y . c. What happens - Mathematically. The estimator that you use is: 1 2 1 ˆ i i 1 i x y = x a This will be biased To show this take the expected value of the estimator and use the
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This note was uploaded on 12/08/2011 for the course ECON 312 taught by Professor Daniellass during the Winter '10 term at UMass (Amherst).

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