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Unformatted text preview: Professor Mumford Econ 360  Fall 2010 [email protected] Problem Set 4 Due at the beginning of class on Tuesday, September 21 True/False (10 points) Please write the entire word. No explanations are required. 1. Heteroskedasticity causes the OLS estimator to be biased. 2. A violation of the zero conditional mean assumption would cause the OLS estimator to be biased. 3. Omitting an important variable that is correlated with the regressor of interest would cause the OLS estimator to be biased. 4. Including an irrelevant variable that is correlated with the regressor of interest would cause the OLS estimator to be biased. 5. A sample correlation coefficient of 0.95 between the regressor of interest and another regressor in the model would cause the OLS estimator to be biased. Long Answer Questions (90 points) 6. (10 points) For each of the 5 GaussMarkov assumptions in the multiple regression model (MLR.1  MLR.5), state the assumption and explain how it could be violated by describing an example in which the assumption does not hold (graphically if desired). (a) MLR.1 (b) MLR.2 (c) MLR.3 (d) MLR.4 (e) MLR.5 7. (10 points) Under assumption MLR.1  MLR.5, derive the variance of the OLS estima tor ˆ β j in the multiple regression model. Express the variance in terms of the Rsquared from the regression of x j on the other independent variables. 1 8. (12 points) Consider the simple regression model: y = β + β 1 x + u....
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 Spring '10
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 Statistics, Regression Analysis, Bias of an estimator

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