Fall 2026

Introductory Econometrics

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

offering recorded4 credits
Recorded instructors · Fall 2026 Kohei Yata3.3/5Kenta Fukuda

Summary

1 / 7

Some students find Yata's lectures confusing and recommend skipping them to study independently using textbooks and past papers.

Grade history

↗
average GPA
letter grades
A
AB
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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

Fall 2026
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 2.43/5 from 7 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.

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

Kohei Yata is criticized for confusing, slide-reading lectures and a strong accent that hinders engagement. Reviewers report that his teaching style makes concepts difficult to understand, with one student claiming he lacked material knowledge.

Kohei Yata provides helpful slides and practice exams that closely mirror tests, making the material predictable. Students appreciate his inclusion of modern research and accessibility, though some find lectures dry.

Recent recorded grades — Fall 2023: 3.25 GPA, 52.0% A/AB (n=75 letter grades); Fall 2024: 3.33 GPA, 57.8% A/AB (n=83 letter grades); Fall 2025: 3.34 GPA, 54.9% A/AB (n=91 letter grades).

Historical instructors & teaching patterns

Historical reviews for Jean-Francois Houde and Christopher Taber show mixed results, with some finding lectures vague or unhelpful while others appreciated clear explanations. Christopher McKelvey and Mikkel Soelvsten are frequently cited as engaging and helpful, though their fast-paced lectures require students to review slides and textbooks in advance to keep up with the difficult material.

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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction92 / 9611
DIS 302Classroom Instruction22 / 241
DIS 303Classroom Instruction24 / 247
DIS 301Classroom Instruction23 / 241
DIS 304Classroom Instruction23 / 242

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

Meeting source records
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Student experience

the class

Kohei Yata provides helpful slides and predictable exams that prepare students well, though some find his lectures boring.

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

Some students find Yata's lectures confusing and recommend skipping them to study independently using textbooks and past papers.

Reviewers note Yata is accessible during office hours and knowledgeable, answering questions beyond the course scope.

Topics

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

Skills

  • Statistical methods for economic research
  • Survey methods and data sourcing
  • Multiple regression and ANOVA
  • Econometric estimation
  • Forecasting and policy applications

Grades

Historical instructor

Fall 2026 · 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 · 0.11 GPA average error.

Grades over time

Through Fall 2026

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

1283 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.

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Selected offering source records
ECON 410 · Fall 2026

Introductory Econometrics

Recorded 2026-09-07
Raw records 
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:296:005602",
    "course_id": "ECON 410",
    "course_uid": "course_45144ea1ab61fccd3dc0a3f6",
    "term_id": "1272",
    "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
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "0e1e09aaa47e036a63a1acec908303213b819a0673af22ce9b854bba7049cb63",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}