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
Documents in this Packet
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Lecture 28: Estimation of the Multiple Regression Model
1 pages
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Lecture 27: Instrumental Variables
1 pages
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Lecture 26: Counterfactual in Policies
2 pages
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Lecture 25: Difference in Differences
1 pages
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Lecture 24: Counterfactual Averages
2 pages
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1 pages
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Lecture 22: Dependent Variable Errors
1 pages
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Lecture 21: Specification and Data Issues
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2 pages
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2 pages
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Lecture 18: Generalized Least Squares and Feasible Generalized Least Squares
2 pages
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2 pages
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Lecture 16: The Linear Probability Model
1 pages
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Lecture 15: Omitted Variable Bias with Many Regressors
2 pages
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Lecture 14: Omitted Variable Bias with Many Regressors
2 pages
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Lecture 13: Unbiasedness of Multivariate OLS Estimators
2 pages
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2 pages
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Lecture 11: I. Goodness of Fit II. Unbiasedness
2 pages
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Lecture 9: One Sided Tests, Confidence Intervals
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2 pages
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6 pages
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Lecture 7: One Sided Tests, Confidence Intervals
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2 pages
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Lecture 5: Asymptotic Normality
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2 pages
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2 pages
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Lecture 2: Marginal Distributions
2 pages
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2 pages
Unformatted text preview:
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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ECON 3120: Exam 2
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