Previous midterms

Year Answer Key
2023 Fall Answers
2023 A02 Answers
2023 A01 Answers
2022 Answers
2021 Answers
2020 Answers
2018 Answers
2015F Answers
2015W Answers
2014 Answers
2013 Answers

Previous finals

Year Answer Key
2023  
2022 Answers
2020 Answers
2019  
2015 Answers
2014 Answers
2013 Answers

Final exam review topics (from Winter 2023)

Chapter 6

  • Why we need multiple “X” variables - OVB
  • OVB: what it is, when it happens, and why
    • house price/fireplace example
  • How the formula for the $b$ are obtained
  • Interpreting the $\beta$

Issues that arise in the multiple regression model:

  • No perfect multicollinearity / DVT
    • define it
    • examples: different units/gender dummy/location dummy
  • Imperfect multicollinearity - basic definition and consequences
  • $R^2$ no longer works
    • explain why
  • $\bar{R}^2$ used instead
    • explain why it works

Chapter 7

  • Joint hypothesis test
  • t-test can’t be used - explain why
  • F-test
  • Restricted/unrestricted models
  • Why the F-test formula with $R^2_U$ and $R^2_R$ makes sense
  • Identifying models under $H_0$ and $H_A$
  • CIs don’t extend well for joint hypothesis tests (confidence sets)
  • Overall F-test
  • How/why to eliminate variables from a model (model selection/building)
  • Presenting models in tables

Chapter 8

  • Linear vs. non-linear effect
  • Consequences of missing a non-linear effect
  • Polynomial regressionn model
    • Determine “r”
    • Interpret the $b$ by predicting values ($\hat{y}$)
  • Logs
    • Can “linearize” exponential growth (GDP)
    • Can “linearize” multiplicative models (Cobb-Douglas)
    • approximate percentage changes
    • 3 configurations: lin-log, log-lin, log-log
    • Intepretations of the $\beta$ in these three configurations

Chapter 9

  • Interaction terms
    • $D \times X$ allows for different slopes/lines for different groups
    • Test for differences between groups
  • Dummy-dummy interactions
    • different effect for different groups
  • DiD
    • minimum wage example
    • fundamental problem of causal inference
    • DiD estimator is the $b$ on the dummy-dummy interaction
    • Picture

Chapter 10

  • Hetroskedasticity vs. homoskedasticity
  • What heterosked. and homosked. look like in a plot
  • Implications of heteroskedasticity
  • Fix for heterosked.
    • Robust standard errors
  • Testing for het.

Chapter 11

  • Missing variable problem
  • Instrument, $z$, has two properties
  • IV/2SLS - describe the two stages
  • wage/education example
  • demand example