Spring 2027

Introductory Econometrics

Introductory Econometrics covers statistical methods for applied economic research, including survey methods, multiple regression, ANOVA, econometric estimation, and forecasting.

offering recorded4 credits
Recorded instructors · Spring 2027 Christopher Mckelvey4.5/5

Summary

1 / 6

Past student reviews · Christopher McKelvey

The course was conceptually challenging, with problem sets serving as excellent preparation for exams. Students noted that weekly homeworks did not take too long to complete.

What you’ll be able to do

  • Derive an ordinary least squares (OLS) estimator for a linear regression model.

  • Test theories about the true model using formal hypothesis tests.

  • Evaluate the expected value and variance of an estimator.

  • Prove that the OLS estimator is unbiased, BLUE, and consistent.

  • State the assumptions underpinning OLS, check for violations of these assumptions, determine the consequences of such violations, and – where possible – suggest alternative statistical approaches that are more appropriate given the circumstances.

  • Evaluate the extent to which econometric methods can be used to determine whether a statistical association represents a causal relationship.

  • Use statistical software to apply all of these statistical techniques to analyze the relationship between real-world economic variables.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

ECON 310 and (MATH 217 or 221). Not open to students with credit forECON 400

ECON 410 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

Christopher Mckelvey

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.63/5 from 93 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 4.7/5 raw quality · 3.6/5 difficulty · 22 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Past student reviews · Christopher McKelvey

Christopher Mckelvey taught with animated lectures that made econometrics interesting. Students found exams fair, often open-note or open-book, and weekly homeworks tested conceptual understanding without requiring excessive memorization of difficult equations.

Some students found the class challenging and boring, noting it required remembering a lot of material and that homework was uncorrelated with the textbook. Others felt the difficulty was manageable because the course focused primarily on concepts.

Recent recorded grades — Spring 2024: 3.02 GPA, 44.0% A/AB (n=84 letter grades); Spring 2025: 3.05 GPA, 45.7% A/AB (n=94 letter grades); Spring 2026: 3.05 GPA, 45.5% A/AB (n=88 letter grades).

Historical instructors & teaching patterns

Past student reviews · Kohei Yata

Kohei Yata’s slides prepared students well for exams, though some found his lectures confusing or soft-spoken. One student noted that the tests closely mirrored past exams, which helped with preparation. Another found the lectures made concepts harder to understand, despite the professor being accessible during office hours.

CHRISTOPHER MCKELVEY is recorded teaching in Fall 2011, Spring 2012, Spring 2013, Fall 2013, Spring 2015, Fall 2015, Spring 2016, Fall 2017, Fall 2019, Spring 2020, Fall 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

CHRISTOPHER R TABER is recorded teaching in Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2014. Recorded history may be incomplete and does not establish a future schedule.

JEAN-FRANCOIS HOUDE is recorded teaching in Fall 2007, Fall 2008, Fall 2009. Recorded history may be incomplete and does not establish a future schedule.

KOHEI YATA is recorded teaching in Fall 2022, Fall 2023, Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

MIKKEL SOELVSTEN is recorded teaching in Fall 2018, Fall 2021. Recorded history may be incomplete and does not establish a future schedule.

XIAOXIA SHI is recorded teaching in Fall 2010, Spring 2012, Fall 2012, Fall 2013, Fall 2016. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 960
DIS 301Classroom Instruction0 / 240
DIS 302Classroom Instruction0 / 240
DIS 303Classroom Instruction0 / 240
DIS 304Classroom Instruction0 / 240

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

Student experience

the class

Past student reviews · Christopher McKelvey

Professor McKelvey taught this econometrics course with animated lectures that made the material engaging. Students found his delivery outstanding for explaining difficult concepts clearly.

Recent recorded grades — Spring 2025: 3.05 GPA, 45.7% A/AB (n=94 letter grades); Fall 2025: 3.34 GPA, 54.9% A/AB (n=91 letter grades); Spring 2026: 3.05 GPA, 45.5% A/AB (n=88 letter grades).

difficulty & workload

Past student reviews · Christopher McKelvey

The course was conceptually challenging, with problem sets serving as excellent preparation for exams. Students noted that weekly homeworks did not take too long to complete.

Past student reviews · Christopher McKelvey

Exams were fair and allowed the use of a notecard, reducing the need to memorize difficult equations. McKelvey was kind, responsive to emails, and good at answering questions in lecture.

Topics

  • Survey methods and data sources
  • Multiple regression and analysis of variance
  • Econometric estimation
  • Forecasting and policy applications

Skills

  • Derive OLS estimators for linear regression models
  • Apply statistical techniques using software to analyze economic variables
  • Evaluate causal interpretation of econometric results

Grades

Historical instructor

Spring 2027 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

8 earlier forecasts · 0.12 GPA average error.

Grades over time

Through Spring 2027

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records
ECON 410 · Spring 2027

Introductory Econometrics

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:296:005602",
    "course_id": "ECON 410",
    "course_uid": "course_45144ea1ab61fccd3dc0a3f6",
    "term_id": "1274",
    "source_course_id": "005602",
    "source_subject_id": "296",
    "title": "Introductory Econometrics",
    "credits_min": 4,
    "credits_max": 4,
    "typically_offered": "Fall, Spring"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1Download part 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "d6d5f7aa1642138876fb988bbea0d0b189e3cfc5232b14e70665b5fbd9e58106",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}