ACE 592

Spring 2026 All Classes

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Special Topics

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Credit: 0 TO 8 hours.

Group instruction on a special topic under the direction of one or more members of the faculty.

Approved for both letter and S/U grading. May be repeated in a semester to a maximum of 8 hours. May be repeated to a maximum of 24 total hours, if topics vary.

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ACE 592 class schedule data for spring 2026
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
30666	Lecture-Discussion	ECM	1:30PM -3:20PM	TR	422 Mumford Hall	Cardoso, D	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Empirical Computation Methods Section Info: Empirical Computational Methods for Applied Economics prepares students to apply core computational methods for solving and estimating economic models. This course is divided into two parts. The first part covers numerical methods commonly used in solving economic models when parameters are known. These methods include numerical differentiation and integration, systems of equations, function approximation, and optimization. The second part builds on these methods, linking economic theory and econometrics by incorporating data to estimate model parameters. This part covers structural econometric modeling and estimation methods, including maximum likelihood, generalized method of moments, and simulation-based estimation. The techniques covered in this course are applicable to a wide range of problems in applied economics, such as optimal decision-making under uncertainty, computable general equilibrium, and dynamic optimization, as well as structural estimation of random utility models and imperfect competition with product differentiation. Restriction(s): Restricted to Graduate - Urbana-Champaign.
58352	Conference	GIE	ARRANGED	n.a.	Location Pending	March, L Paulson, N	Part of Term: 1 Date Range: 01/20/26-05/06/26 Special Approval: Instructor Approval Required Section Info: Graduate Internship Experience. Please note, this course is hybrid, and is designed with on-campus engagement in mind, including career fair attendance and other on campus assessments.
45250	Lecture-Discussion	SAE	3:30PM -5:10PM	TR	316N Mumford Hall	Hutchins, J	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Data Science for Applied Econo Section Info: This course will teach data science tools and workflow for analysis of questions in applied economics. Using the python programming language and git version control, we will cover obtaining data via scraping and APIs, processing and cleaning data using python, and analyzing data via data visualization and basic machine learning techniques. The course will broadly cover the basics of text, spatial, and numeric data with an emphasis on their uses in analyzing economic questions and conducting research. Restriction(s): Restricted to Graduate - Urbana-Champaign.