Final Review
ECON 480 • Econometrics • Fall 2022
Dr. Ryan Safner
Associate Professor of Economics
safner@hood.edu
ryansafner/metricsF22
metricsF22.classes.ryansafner.com
Major Models & Extensions
- Causality
- Fundamental problem of causal inference, potential outcomes
- DAGs, front-doors/back-doors, controlling
- Multivariate OLS
- Omitted Variable Bias
- Variance/Multicollinearity
Major Models & Extensions
- Categorical data
- Interpreting dummies, group means
- Using categorical variables as dummies
- dummy variable trap
- interaction effects
- Nonlinear Models & Transforming Variables
- quadratic model
- higher-order polynomials
- logs
- standardizing variables
- joint hypothesis (F-tests)
Major Models & Extensions
Panel data
- pooled regression & problems
- fixed effects
Difference-in-differences
Instrumental variables
Question 1
What are the two conditions for a variable Z to cause .shout[omitted variable bias] if it is left out of the regression?
Question 2
Wagesi=β0+β1Educationi+β2Agei+β3Experiencei+ui
Suppose Educationi and Agei are highly correlated
- Does this bias ^β1 and ^β2?
- What will happen to the variance of ^β1 and ^β2?
- How can we measure this?
Question 3
Cholesteroli=β0+β1Treatedi+ui
- Treatedi is a dummy variable ={1if person received treatment0if person did not receive treatment
- What is ^β0?
- What is ^β1?
- What is the average cholesterol level for someone who recieved treatment?
Question 4
Yi=β0+β1Redi+β2Orangei+β3Yellowi+β4Greeni+β5Bluei
Suppose the color of observation i can be either {Red, Orange, Yellow, Green, Blue, Purple }
- What is ^β0?
- What is ^β1?
- What is the average value of Yi for Green observations?
- Why can’t we add β6Purplei?
Question 5
^Utilityi=β0+β1Eggsi+β2Breakfasti+β3(Eggsi×Breakfasti)
Breakfasti is a dummy variable ={1if meal i is breakfast0if meal i is not breakfast
- What is ^β1?
- What is ^β2?
- What is ^β3?
- We have two regressions (one for Breakfast; one for Not Breakfast)
- how can we determine if the intercepts are different?
- how can we determine if the slopes are different?
Question 6
^Utilityi=2+4 Ice Cream Conesi−1 Ice Cream Cones2i
- What is the marginal effect of eating 1 more Ice Cream Cone?
- What if we start with 1 Ice Cream Cone?
- What if we start with 4 Ice Cream Cones?
- What amount of ice cream cones will maximize utility?
- How would we know if we should add Ice Cream Cones3i?
Question 7
ln(GDPi)=10+2 population (in billions)i
- Interpret ^β1 in context.
ln(GDPi)=10+0.1ln(populationi)
- Interpret ^β1 in context.
Question 8
- Explain what an F-test is used for.
- Explain how an F-statistic is estimated (roughly).
Question 9
Consider a two-way fixed effects model:
Divorce Rateit=β1Divorce Lawit+αi+θt+ϵit
for State i at time t
- Why do we need αi and θt?
- What sorts of things are in αi?
- What sorts of things are in θt?
Question 10
Suppose Maryland passes a law (and other States do not) that affects crime rates. Consider the following model:
Crime Rateit=β0+β1Marylandi+β2Aftert+β3(Marylandi×Aftert)
for State i at time t
- What must we assume about Maryland over time?
- What is the average crime rate for other states before the law?
- What is the average crime rate for Maryland after the law?
- What is the causal effect of passing the law?
Question 12
- What are the two conditions required for an instrument to be valid?
- How is this different from the conditions for omitted variable bias?
- How can we test each condition?
- How do we run a two-stage least squares regression?
