Spring 2027

Economic Forecasting

Introduction to econometric time series analysis, forecasting methods, and forecast evaluation with theoretical, methodological, and applied hands-on data analysis.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 8

Past student reviews · Bruce Hansen

One student described the class as stress-free, citing lenient grading and relatively easy exams, alongside a final project they found cool.

What you’ll be able to do

  • explain and use linear time-series econometric models

  • describe and use economic time-series data and data sources

  • formulate, estimate, and evaluate models for economic forecasting

  • make point forecasts, interval forecasts and fan charts

  • select and use conditioning information

  • use model selection and combination methods

  • be a "fox" rather than a "hedgehog"

  • explain, formulate, and use Vector Autoregressive time-series models

  • explain, formulate, and use multivariate time-series forecasting models

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
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All recorded terms · compare terms & instructors

Prerequisites

Course map

ECON 400, 410, or graduate/professional standing

ECON 460

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

BRUCE HANSEN is recorded teaching in Spring 2015, Spring 2017, Fall 2018, Fall 2020. 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

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SectionModeEnrolled / capacityWaitlist

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

Meeting source records
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Student experience

the class

Past student reviews · Bruce Hansen

Students found the course rewarding, noting that Bruce Hansen effectively explained complex material at an undergraduate level while providing marketable skills.

Recent recorded grades — Spring 2022: 3.52 GPA, 75.0% A/AB (n=40 letter grades); Fall 2024: 3.61 GPA, 84.7% A/AB (n=72 letter grades); Fall 2025: 3.63 GPA, 82.1% A/AB (n=39 letter grades).

difficulty & workload

Past student reviews · Bruce Hansen

One student described the class as stress-free, citing lenient grading and relatively easy exams, alongside a final project they found cool.

Past student reviews · Bruce Hansen

The class involved applied work with time-series data, which students considered useful and a source of marketable skills.

Topics

  • Linear time-series econometric models
  • Forecasting methods and evaluation, including point forecasts, interval forecasts, and fan charts
  • Vector Autoregressive and multivariate time-series forecasting models
  • Model selection and combination methods

Skills

  • Explain, formulate, and use linear and Vector Autoregressive time-series econometric models.
  • Formulate, estimate, and evaluate economic forecasting models to produce point forecasts, interval forecasts, and fan charts.
  • Describe and use economic time-series data and sources through hands-on data analysis.
  • Select and combine forecasting models using appropriate methods.

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · 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

1316 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

No offering records for the selected term.

Raw records 
[]
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 2026-10-14

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

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "2ea8670ca25fd86c1047436045fe9f47c8db1a1830f62a9400c42db2b696316b",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}