Lecture 16 2010

Lecture 16 2010 - IV. MultipleRegression. A. Introduction...

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1 IV. Multiple Regression . A. Introduction B. CRM C. Estimation D. Interpretation of Parameter Estimates E. Properties of Estimators IV. Multiple Regression A. Introduction . 1. Multiple regression – measure the effects of several independent variables on the dependent variable. 2 Write a multiple regression population 2. regression equation: Y i = β 0 + 1 X 1i + 2 X 2i +…+ k X ki + u i . B. Classical Regression Model –aga in the assumptions, and we add one. 1. Y i = 0 + 1 X 1i + 2 X 2i + … k X ki + u i . 2. The Xs are not random variables. 3. E[ u | X i ] = 0. 4. Var(u | X i ] = σ 2 . 5. Cov( u i , u j ) = E[u i u j ] = 0. 6. u ~ Normally. 7. No perfect linear associations among Xs.
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2 C. Estimation of population parameters. 1. How? What method?? 2. Process: Describe in words. D. Interpretation of Parameter Estimates. 1. Partial Effects–we now have several variables affecting Y. Each X has a partial effect on Y . Example: Y = f(X 1 , X 2 ) What do we do in econometrics? 2. Experimental vs Social Sciences ± Experimental Sciences – in physical and
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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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Lecture 16 2010 - IV. MultipleRegression. A. Introduction...

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