Fall 2025 Midterm info:

The midterm will cover up to and including Chapter 5.1. Bring a non-programmable calculator, student ID, and writing implements. The exam takes place in the regular classroom at the regular time, on October 7th.

Note: in previous years, we only had one midterm. You have two. This means that the sample midterms contain additional material that has not yet been covered in this course. You should ignore the follow questions:

  • 2023 Fall: 6, 7, 9, 11
  • 2023 A02: 7, 8, 10, 11(c), 11(d), 12
  • 2023 A01: 7, 8, 10, 11(c), 11(d), 12
  • 2022: 7, 8, 10(d), 11
  • 2021: All questions are appropriate, but are difficult (this was an open-book, online exam).
  • 2020: 9, 10(d), 11
  • Going back further than 2020 is risky.

You will see in the sample midterms that you will be provided with a formula sheet and table of standard normal probabilities.

Ignore the following practice questions from the end of each chapter:

  • Ch.2: 3
  • Ch.3: 3, 4, 5
  • Ch.5: 3-7

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

Chapter 2

  • Define / calculate (true) mean (from a prob. function)
  • Same for variance
  • Properties of mean / variance (if you multiply by $c$, variance is multiplied by $c^2$)
  • Know what cov. and corr. are measuring (very similar - corr. is between -1 and 1)
  • Skip blizzard/midterm example
  • Basic result of CLT, and how it applies to sample average and LS estimator (it makes them Normal)

Chapter 3

  • Estimators (y-bar, LS intercept and slope) are random variables. Explain why
  • Bias / efficiency / consistency (define these terms either in a sentence or an equation)
  • Know that $\bar{y}$ and LS is unbiased / efficient / consistent under some assumptions.
  • GM theorem - establishes efficiency
  • Define: significance, type I and II error, critical value, confidence interval, p-value
  • Basics of hypothesis testing
  • Why t-test instead of z-test, and how the distribution changes from Normal to t when we use $s^2$
  • $\hat{\sigma}^2$ (biased) and $s^2$ (unbiased)
  • degrees of freedom

Chapter 4

  • Most models from econ are straight lines (linear)
  • Begin to define the components of the pop. model
  • The importance of the error term (epsilon)
  • Predicted values and residuals
  • Least squares is derived by min. sum of squared residuals
  • LS is unbiased / efficient / consistent (don’t bother with the 6 assumptions)

Chapter 5

  • R-square: define it. Pick it out in R output
  • To derive: take sample variance of both sides of $y = \hat{y} + e$
  • TSS / ESS / RSS terminology

  • no fit / perfect fit (draw diagram)

  • Hypothesis testing: calculate a t-stat, get the decision to reject/fail to reject correct.
  • Reject H0 when: p-value is small (< 0.05) / t-stat is greater than crit. value (e.g. 1.96) / if hypothesis is outside the C.I.

  • var(b1) tells us when the LS estimator becomes more efficient (3 things)

  • Definition of dummy variables
  • Interpretating the beta on a dummy variable

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