# Exam 2 Study Guide

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## Exam 2 Study Guide

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study guide

- Pages:
- 3
- Type:
- Study Guide
- School:
- Cornell University
- Course:
- Econ 3120 - Applied Econometrics
- Edition:
- 1

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Econ 3120 1st Edition Exam 2 Study Guide In three or fewer sentences describe each of the following concepts Do not use or simply translate any formulas a Gauss Markov Theorem Answer The Gauss Markov Theorem states that under assumptions MLR 1 MLR 5 the OLS estimators are the best minimum variance linear unbiased estimators b Collinearity Answer In the linear regression context collinearity occurs when one independent variable can be expressed as a linear combination of the other independent variables When this occurs the OLS estimates for the collinear variables cannot be computed c Slope parameter Answer The slope parameter tells us how the dependent variable changes when there is a change in the independent variable It may be interpreted differently based on the functional forms of both the dependent and independent variables d Explained sum of squares SSE Answer The explained sum of squares SSE is the sample variation in the predicted values that result from a regression It captures the variation in the dependent variable that can be explained by the independent variable 2 Suppose you estimate the following model of weight on height for a random sample of 20 year old men Weight 0 1 Height u where Weight is measured in pounds and Height is measured in inches a Give an interpretation of the estimated slope coefficient 1 Answer The slope coefficient 1 is the change in Weight in pounds for a one inch change in Height b Suppose instead that you estimate log Weight 0 1 Height Now what is the interpretation of the estimated slope coefficient Answer The slope coefficient 1 is the percentage change in Weight for a one inch change in Height c Finally suppose you estimate log Weight 0 1 log Height What is the interpretation of the estimated slope coefficient in this case Answer The slope coefficient 1 is the percentage change in Weight for a onepercent change in Height 3 Consider the wage regression model log wage 0 1educ 2exper 3exper2 u where exper represents the worker

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