ECON410: pass the exams, not just read the notes
Your complete guide to University of Wisconsin-Madison's introductory econometrics course. See where the marks are, work real practice questions, and study with an AI tutor that knows ECON410.
Sia generates ECON410 practice questions, walks through statistics review and simple regression step by step, and quizzes you on the material the exam weights most heavily.
Worked example
In the simple regression y = β0 + β1x + u, under the Gauss-Markov assumptions, what is Var(β̂1)?
Write β̂1 = β1 + Σ(xi − x̄)ui / Σ(xi − x̄)².
Divide by [Σ(xi − x̄)²]² to get σ² / Σ(xi − x̄)².
Options B and C mis-handle the denominator; option D confuses σ with σ². The result shows precision rises with regressor spread and sample size.
The trap: Writing σ²/n from the sample-mean formula. The regressor's total variation, not n alone, drives the slope's precision — the course flags this in the simple-regression derivations. classic slip!
One exam decides 24% of your grade. Accommodation only for documented, pre-notified circumstances. This whole page is built around that.
Overview
What ECON410 is, and where it sits
ECON 410 Introductory Econometrics is UW-Madison's theory-first econometrics course, required for the mathematics-emphasis and honors economics majors and the alternative to the applied ECON 400. This guide follows the Spring 2026 offering taught by Christopher McKelvey; the course is also offered in summer and fall.
The syllabus derives and proves results wherever possible: twenty-four lectures move from a statistics review through simple and multiple regression, inference, asymptotics, dummy variables, heteroskedasticity and data issues, then time series, difference-in-differences, panel data, instrumental variables and limited dependent variables, following Wooldridge. Stata is used in lectures, discussion sections and problem sets.
The Spring 2026 grade was 8% attendance, two in-class midterms of 24% each, a 24% cumulative final and 20% from five problem sets. Grades are curved upward only, the better of a fixed percentage scale and a percentile scale.
Always treat your own course outline and the exam timetable as authoritative.
Difficulty & time commitment
Is ECON410 hard, and how much time does it take?
ECON410 is manageable if you keep a weekly rhythm and treat the back half as the main event. The pattern is consistent: it starts gently and steepens, and the heaviest assessment is the part that separates grades.
The difficulty curve and the assessment weighting point the same way: the back half is harder and worth more. Front-loading effort there is the highest-return decision in the course.
Is this course for you
Who tends to do well, and who tends to struggle
You will likely do well if
- You are comfortable with calculus from MATH 221 and want to see where the formulas come from.
- You can write a short proof under exam conditions.
- You attend every class; 8% is for being there and the derivations build cumulatively.
- You start each problem set early enough to debug Stata.
You may struggle if
- You wanted applied econometrics without proofs; that is ECON 400.
- You memorise formulas rather than derive them; exams test the derivation.
- You miss a midterm without documentation; each is 24% and accommodation needs advance notice.
- You rely on AI for written answers; the syllabus zeros them.
- Rederive the OLS estimator and its variance from scratch until it is automatic.
- State the Gauss-Markov assumptions in your own words and know which one each later topic relaxes.
- Keep a proofs sheet: unbiasedness, BLUE, consistency.
- Work each problem set on paper first, then in Stata, so the mechanics and the software agree.
Syllabus
The 11 topics, block by block
The exam-weight marker on each topic shows where the marks concentrate. The amber topics carry the highest exam weight.
T1 · Statistics review
Lecture 2; Wooldridge App. BRandom variables, estimators, sampling distributions.
T2 · Simple regression
Lectures 3-6; Wooldridge ch. 2Derivation of OLS, algebraic properties, expected value and variance of the estimator.
T3 · Multiple regression
Lectures 7-9; Wooldridge ch. 3Estimation, partialling out, omitted variable bias, the Gauss-Markov theorem.
T4 · Inference
Lectures 10-12; Wooldridge ch. 4t tests, confidence intervals, F tests and joint hypotheses.
T5 · Asymptotics
Lecture 13; Wooldridge ch. 5Consistency and large-sample inference.
T6 · Dummy variables
Lectures 14-15; Wooldridge ch. 7Binary regressors, interactions, the linear probability model.
T7 · Heteroskedasticity
Lectures 16-17; Wooldridge ch. 8Consequences, robust inference, testing and weighted least squares.
T8 · Data issues and time series
Lectures 18-19; Wooldridge ch. 9-10Measurement error, outliers; static and finite-distributed-lag time-series models.
T9 · Difference-in-differences and panel data
Lectures 20-22; Wooldridge ch. 13-14Pooled cross sections, two-period panels, fixed effects.
T10 · Instrumental variables
Lecture 23; Wooldridge ch. 15Endogeneity, the IV estimator and its conditions.
T11 · Limited dependent variables
Lecture 24; Wooldridge ch. 7, 17Binary outcomes beyond the linear probability model.
How it's assessed
Assessment structure
| Component | Weight | Format & timing |
|---|---|---|
| Attendance (TopHat, lectures and discussion) | 8% | TopHat check-in at every non-exam lecture and discussion section; twelve excused absences. Every class meeting. Location-verified attendance. |
| Midterm exam 1 (in class, 26 February 2026) | 24% | In-class midterm on simple and multiple regression, inference and asymptotics. 26 February 2026. Accommodation only for documented, pre-notified circumstances. |
| Midterm exam 2 (in class, 9 April 2026) | 24% | In-class midterm on dummy variables, heteroskedasticity, data issues and time series. 9 April 2026. Same accommodation rule. |
| Final exam (cumulative, 5 May 2026, 2:45-4:45pm) | 24% | Cumulative final in the university exam block. 5 May 2026, 2:45-4:45pm. In person. |
| Five problem sets (Stata log required) | 20% | Five problem sets with Stata logs, submitted on paper by 10:45am on the due date. Across the semester. 24-hour late window with 20% deduction; AI-written answers receive zero. |
- The five components sum to 100 and there is no separate hurdle. The curve is one-directional: the higher of the percentage scale (A from 92%) and the percentile scale (A for the top 20%) is awarded. Problem sets are accepted up to 24 hours late with a 20% deduction, and identical or AI-written free-response answers score zero.
- Two in-class midterms (26 February and 9 April 2026) covering the material since the previous exam, and a cumulative two-hour final (5 May 2026). All in person; accommodation requires documented, pre-notified circumstances.
This is an exam-cram course. With the exams at 72% of the grade and the midterm exam 1 (in class, 26 february 2026) alone at 24%, your result is overwhelmingly decided by how well you perform under time pressure. Accommodation only for documented, pre-notified circumstances.
Final exam timing: During the examination period. Confirm the exact date and venue on your exam timetable.
How to actually pass it
A weekly rhythm, two checklists, and the traps to avoid
The course rewards consistency over cramming, and practice over re-reading. Here is the loop that works, then what to have nailed before each exam.
The weekly loop
Before the mid-semester checklist
- Derive the OLS slope and show it is unbiased.
- Compute the variance of the OLS estimator under homoskedasticity.
- Explain omitted variable bias and its direction.
- Run a t test and an F test and interpret them.
Before the final heaviest topics
- Explain consistency and when large-sample inference applies.
- Test for and correct heteroskedasticity.
- Set up fixed-effects and difference-in-differences models.
- State the IV conditions and interpret an IV estimate.
The mistakes that cost marks
Skipping the derivation. Outcomes ask you to prove, not describe; a formula without its derivation loses most of the marks.
Wrong bias direction. Omitted variable bias questions require both correlations; state each before signing the bias.
Paper deadline. Problem sets are due on paper at 10:45am, not in Canvas alone.
Teaching team
Who teaches ECON410
The bios below are factual. We do not rate lecturers; any star ratings are submitted by students who have taken ECON410.
Teaching team as listed in the course materials reviewed. AskSia does not rate lecturers; star ratings are submitted by students who have taken ECON410.
Formula & concept sheet
The vocabulary and formulas you must own
- OLS
- Ordinary least squares, derived by minimising the sum of squared residuals.
- Unbiasedness
- The estimator's expected value equals the true parameter under the Gauss-Markov assumptions.
- BLUE
- Best linear unbiased estimator: OLS has the smallest variance among linear unbiased estimators.
- Consistency
- The estimator converges to the true value as the sample grows.
- Omitted variable bias
- Bias from excluding a regressor correlated with both included regressors and the error.
- F test
- A joint test of several restrictions.
- Heteroskedasticity
- Non-constant error variance; OLS stays unbiased but its usual standard errors are wrong.
- Fixed effects
- Unit-specific intercepts absorbing time-invariant confounders.
- Instrumental variable
- A variable correlated with the endogenous regressor but not with the error.
- Linear probability model
- OLS with a binary outcome.
Set texts
The prescribed reading
The syllabus references map straight onto these.
Introductory Econometrics: A Modern Approach
.
Where it fits
Prerequisites, related courses & why it matters
Prerequisites: ECON 310 and MATH 221. 4 credits; two 75-minute lectures and one 50-minute discussion per week. Offered Spring, Summer and Fall; this guide follows Spring 2026.
Your ECON410 study toolkit
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FAQ
Frequently asked questions
Is ECON 410 hard?
Moderate on the six-factor rubric, at the harder end: three exams carry 72% and the outcomes require derivations and proofs, though the curve can only help and 28% is attendance plus problem sets.
What is the assessment breakdown?
Attendance 8%, two midterms at 24% each, final exam 24% and five problem sets 20%, per the instructor's Spring 2026 syllabus.
ECON 410 or ECON 400?
Alternatives, not a sequence. 410 is mathematical and proof-based and required for math-emphasis and honors majors; 400 is applied.
What are the prerequisites?
ECON 310 and MATH 221.
Who taught it?
Christopher McKelvey (Lecturer) in Spring 2026, with teaching assistant Satyen Pandita.
Can I use AI on problem sets?
No. The syllabus states that free-response answers written with AI or an electronic translator, or with identical wording to another student's, receive zero credit.
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