ECON 549: Computational Economics

Time and Location

Monday/Thursday, 8:30-10:00 am, BEC 363

Instructor

Dr G. Cornelis van Kooten

Office: BEC 372

Office hours: Fri 8:30-9:45 am and by appointment

Synopsis

The objective of this course is to introduce methods from operations research for addressing real-world economic problems. Operations research is a broad set of computational tools. We will consider traditional mathematical programming models (linear programming, non-linear programming, integer programming), but extend these to the realm of dynamic optimization models, including deterministic and stochastic dynamic programming. The focus is on numerical solutions of models. Students will be expected to program problems in a Matlab and GAMS software environment.

Grading Textbooks Course Outline

Class Assignments

Grading

Item	Weight towards grade
Homework Assignments	30%
Term Project	40%
Class Presentation	5%
Final Examination	25%

There will be some 4 to 6 homework assignments. Homework assignments are generally due within a week (unless otherwise noted). Homework assignments can be downloaded from the main course website and students must ensure that their completed assignments constitute independent work. Further, copying work from other students during examinations could result in a failing grade regardless of performance on other class components (see Academic Integrity).

Students' grades will be converted to a UVic letter grade as indicated below. Notice that no E grades will be assigned. There is no retake exam for students that fail the course. Grading will be based on the performance of each individual student only. There will be no marking 'on the curve' as per the policy of the Department of Economics.

A⁺	³ 90%	B⁺	75-79%	C⁺	60-64%	F	<50%
A	85-89%	B	70-74%	C	55-59%
A^-	80-84%	B^-	65-69%	D	50-54%

Note: B^- is considered an inadequate grade for graduate students.

Academic Integrity (students should consult the latest Guidelines on Academic Integrity)

Textbooks:

Conrad, Jon M. and Colin W. Clark, 1987. Natural Resource Economics Notes and Problems. Cambridge, UK: Cambridge University Press. (denoted C&C)

Howitt, Richard E., 2005. Agricultural and Environmental Policy Models: Calibration, Estimation and Optimization. Davis, CA: Department of Agricultrual Economics. Draft January 18, 2005. pp.207.

Kendrick, David A., 2002. Stochastic Control for Economic Models. 2^nd edition. 297pp.

Kendrick, David A., P. Ruben Mercado and Hans M. Amman, 2006. Computational Economics. Princeton, NJ and Oxford, UK: Princeton University Press. 436pp.

McCarl, Bruce A. and Thomas H. Spreen, 2004. Applied Mathematical Programming using Algebraic Systems. College Station, TX: Department of Agricultural Economics, Texas A&M University. 567pp.

McCarl, Bruce A., Alex Meeraus, Paul van der Eijk, Michael Bussieck, Steven Dirkse and Pete Steacy, 2007. McCarl GAMS User Guide. Version 22.5. College Station, TX: Department of Agricultural Economics, Texas A&M University. (denoted User Guide).

Meerschaert, Mark M., 1999. Mathematical Modeling. 2nd Edition. San Diego: Academic Press.

Schoney, Richard A., 2006. Computational Economics. Applied Operations Research and Decision Making under Risk and Uncertainty. Saskatoon, Saskatchewan: Department of Agricultural Economics, University of Saskatchewan. 231pp.

Tesfatsion, Leigh and Kenneth L. Judd, 2006. Handbook of Computational Economics. Agent-Based Computational Economics. Volume 2. Amsterdam: Elsevier. (denoted Handbook)

There is no need to purchase a textbook as all material will be made available on the course website or as handouts. Only a few selected pages will be made available from these sources as required.

Tentative Outline:

1. Introduction to computation economics

Introductory lecture

Software for computational economics and operations research

GAMS (GAMS tutorial)
Matlab (Matlab tutorial, Matlab manual)

Readings: Howitt, Chapter 1; M&S Chapter 1

2. Review of linear programming

Review of linear programming (lectures)
Linear Programming in Matlab: An introduction to Matlab (lecture)
Linear Programming in GAMS: An Introduction to GAMS (lecture)
Linking to GAMS from within Matlab (lecture) Students should also consider the manual by Michael Ferris

Readings: Howitt, Chaps 2, 3 & 4 (M&S Chaps 2-6, 9, 10, 15 & 16; Schoney Chaps 1, 2 & 3)

3. Extending linear programming: Approximation, integer and nonlinear programming

Approximation methods and 'linearizing' nonlinear programs
Calibration and positive mathematical programming
Integer programming (IP) & mixed integer programming (MIP)
(lectures for the above three topics)
Methods of nonlinear programming (NLP) (NLP lecture)
Multiple objective programming (MODM lecture)
Positive Mathematical Programming (PMP) (lecture)
Time Dimensions (Optimal control via the backdoor) (lecture)

Readings: Howitt, Chaps 5, 8 & 6.1 & 6.2 (M&S Chaps 11, 12 & 13)

4. Dynamic optimization and programming

Optimal control and dynamic programming (lectures)
Readings: M&S Chap 8; KMA Chap. 16

5. Stochastic models and risk & uncertainty

Stochastic optimization: Continuous time and discrete time (lectures)
Risk and uncertainty in mathematical programming models (lectures)
Readings: Howitt, Chap 6.3; M&S Chap 14; Schoney Chaps 4 & 5

6. Heuristic approaches and simulation (including agent-based models)

Readings: Handbook Chap 17; M&S Chap 14; Schoney Chaps 4, 5 & 6 (lectures)

Class assignments

A link to homework assignments will be provided on the main web page for the course. Students are responsible for obtaining their homework assignments from the website once they have been announced in class.

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