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Final Review Questions

ECON 480 • Econometrics • Fall 2021

Ryan Safner
Assistant Professor of Economics
safner@hood.edu
ryansafner/metricsF21
metricsF21.classes.ryansafner.com

Major Models and Extensions

  • Causality
    • Fundamental problem of causal inference, potential outcomes
    • DAGs, controlling

Major Models and Extensions

  • Causality
    • Fundamental problem of causal inference, potential outcomes
    • DAGs, controlling
  • Multivariate OLS
    • Omitted Variable Bias
    • Variance/Multicollinearity

Major Models and Extensions

  • Causality
    • Fundamental problem of causal inference, potential outcomes
    • DAGs, controlling
  • Multivariate OLS
    • Omitted Variable Bias
    • Variance/Multicollinearity
  • Categorical data
    • Using categorical variables as dummies
    • dummy variable trap
    • interaction effects

Major Models and Extensions

  • Nonlinear Models
    • quadratic model & polynomial models
    • logarithmic models

Major Models and Extensions

  • Nonlinear Models
    • quadratic model & polynomial models
    • logarithmic models
  • Panel Data
    • pooled model
    • fixed effects
    • difference-in-difference models

Question 1

What are the two conditions for a variable Z to cause 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

Question 2

Wagesi=β0+β1Educationi+β2Agei+β3Experiencei+ui

Suppose Educationi and Agei are highly correlated

  • Does this bias ^β1 and ^β2?

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

Question 3

Cholesteroli=β0+β1Treatedi+ui

  • Treatedi is a dummy variable ={1if person received treatment0if person did not receive treatment
  • What is ^β0?

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?

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 observation i can be either {Red, Orange, Yellow, Green, Blue, Purple }

Question 4

Yi=β0+β1Redi+β2Orangei+β3Yellowi+β4Greeni+β5Bluei

Suppose observation i can be either {Red, Orange, Yellow, Green, Blue, Purple }

  • What is ^β0?

Question 4

Yi=β0+β1Redi+β2Orangei+β3Yellowi+β4Greeni+β5Bluei

Suppose observation i can be either {Red, Orange, Yellow, Green, Blue, Purple }

  • What is ^β0?

  • What is ^β1?

Question 4

Yi=β0+β1Redi+β2Orangei+β3Yellowi+β4Greeni+β5Bluei

Suppose 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 shapes?

Question 4

Yi=β0+β1Redi+β2Orangei+β3Yellowi+β4Greeni+β5Bluei

Suppose 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 shapes?

  • 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

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?

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?

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?

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

Question 6

^Utilityi=2+4 Ice Cream Conesi−1 Ice Cream Cones2i

  • What is the marginal effect of eating 1 more Ice Cream Cone?

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?

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?

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?

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.

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;

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

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?

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?

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

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?

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?

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?

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?

Major Models and Extensions

  • Causality
    • Fundamental problem of causal inference, potential outcomes
    • DAGs, controlling
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