Lecture 22 2010

Lecture 22 2010 - V.ExtensionsofMultipleRegression A....

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1 V. Extensions of Multiple Regression A. Dummy Variables B. Scaling Variables C. Non Linear Models 1. Basic Idea for Estimation . 2. Models that don’t require logs 3. Models that require log transform . C. Non Linear Models 1. Basic idea : Make the model look linear in the parameters. Then estimate using OLS. 2. Models that don’t require a log transform . a. Quadratic form: Y = β 0 + 1 X + 2 X 2 + u Examples: Interpretation: wage = - 5.49 + 0.905 ed + 0.309 ex - 0.00352 exsq - 0.726 fe - 0.0903 feex Predictor Coef StDev T P Constant -5.494 1.2140 -4.53 0.000 ed 0.9046 0.0791 11.43 0.000 ex 0.3093 0.0554 5.59 0.000 exsq -0.0035 0.0012 -2.98 0.003 fe -0.7264 0.6725 -1.08 0.281 feex -0.0903 0.0309 -2.93 0.004 60 50 40 30 20 10 0 12.5 11.5 10.5 9.5 8.5 7.5 6.5 5.5 4.5 ex wagehat1 S = 4.388 R-Sq = 27.8% R-Sq(adj) = 27.1%
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2 C. Non Linear Models b. Inverse form: Y = β 0 + 1 (1/X) + u Example : Phillips Curve. Wage Inflation = 0 + 1 (1/Unemp) + u Interpretation : determine the partial effect (derivative) The regression equation is w-dot = - 1.04 + 7.91 INV(U) Predictor Coef SE Coef T P Constant -1.044 2.373 -0.44 0.666 INV(U) 7.907 3.164 2.50 0.025 S = 1.734 R-Sq = 29.4% R-Sq(adj) = 24.7% 0 2 4 6 8 0.5 1 1.5 2 2.5 3 C. Non Linear Models 3. Models that require a log transform .
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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 22 2010 - V.ExtensionsofMultipleRegression A....

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