Spring 2027

Topics in Computational Economics

Topics in Computational Economics covers techniques to compute and estimate structural models, transitioning from formal training to applications.

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

Summary

1 / 4

Recent recorded grades — Fall 2009: 3.47 GPA, 63.6% A/AB (n=33 letter grades); Fall 2024: 3.88 GPA, 96.0% A/AB (n=25 letter grades); Fall 2025: 4.00 GPA, 100.0% A/AB (n=21 letter grades).

What you’ll be able to do

  • Apply numerical methods to solve dynamic programming problems (including parallel computing)

  • Solve and simulate dynamic equilibrium models with heterogenous agents to understand such issues as wealth inequality and the the firm size distribution

  • Use simulated method of moments to calibrate dynamic models with heterogeneous agents

  • Solve and estimate static and dynamic discrete choice models to understand such issues as demand for differentiated products, residential sorting, investment decisions

  • Solve dynamic Markov games with strategic interactions to understand such issues as strategic investments and the evolution of the firm size distribution

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

Graduate/professional standing

  • Graduate/professional standing
ECON 880

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

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

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

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist

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

Meeting source records
Download part 1

Student experience

the class

Recent recorded grades — Fall 2009: 3.47 GPA, 63.6% A/AB (n=33 letter grades); Fall 2024: 3.88 GPA, 96.0% A/AB (n=25 letter grades); Fall 2025: 4.00 GPA, 100.0% A/AB (n=21 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Computing and estimating structural models.
  • Dynamic programming and parallel computing.
  • Dynamic equilibrium models with heterogeneous agents, wealth inequality, and firm size distribution.
  • Static and dynamic discrete choice models, including demand for differentiated products and residential sorting.

Skills

  • Applying numerical methods and parallel computing to solve dynamic programming problems.
  • Solving and simulating dynamic equilibrium models with heterogeneous agents.
  • Using simulated method of moments to calibrate dynamic models.
  • Solving and estimating static and dynamic discrete choice models.
  • Solving dynamic Markov games with strategic interactions.

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

Not enough comparable courses for Spring 2027 in UW–Madison.

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

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

No records available.

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 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "7b7218f032bf7aea546ee830fffb805da11882ecdd50c03d472e86a512dad2d7",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}