EC320_HW4-SOLUTIONS

# EC320_HW4-SOLUTIONS - University of Oregon Department of...

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University of Oregon Rosie Mueller Department of Economics Fall 2016 Homework 4 - SOLUTIONS EC 320: Econometrics Due in Lab Week of 10/31 Please type or write neatly your answers to the following questions on a separate sheet of paper . Points may be deducted for assignments that are a big mess, are unstapled, or are otherwise a pain to deal with. This assignment is due at the beginning of your scheduled lab . Include your scheduled lab time on your written homework. Only a subset of questions will be graded but a full solution key will be provided. Additionally, please complete the Midterm Survey , posted on Canvas. Responses of the survey are anonymous, but completion is worth 20 points towards Homework 4. Multiple Regression Analysis : This week you will begin working with multiple regression models. The details will be explained further in lecture, but for now some basic explanation should suffice. So far, we have dealt with the simple linear regression model: Y i = β 0 + β 1 X i + u i The multiple regression model simply adds more X variables. A typical multiple regression model with k independent variables would look like this: Y i = β 0 + β 1 X 1 i + β 2 X 2 i + . . . + β k X ik + u i Here, i still indexes the i th observation, X 1 i , X 2 i , . . . X ik refer o the different independent variables, and u i is still the disturbance term. The intuition for OLS is still the same with multiple regression models: we find the OLS estimators b 0 , b 1 , . . . b k for β 0 , β 1 . . . , β k that minimize the residual sum of squares n i =1 ( e i ) 2 . The interpretation of the regression parameters in a multiple regression model is very similar to the simple regression model except for one difference. Each slope parameter now measures the

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