Postgraduate Course: Computational Methods in Economics (ECNM11066)

Course Outline
School	School of Economics	College	College of Arts, Humanities and Social Sciences
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)	Availability	Not available to visiting students
SCQF Credits	10	ECTS Credits	5
Summary	This module, optional for all first year PGR students, is designed to introduce and cover in some depth a range of theoretical issues and techniques so as to broaden student's knowledge of computational methods in economics. The aim is to provide exposure to a wide variety of methods that may be used by graduate research students to simulate and solve economic models.
Course description	This module, optional for all first year PGR students, is designed to introduce and cover in some depth a range of theoretical issues and techniques so as to broaden student's knowledge of computational methods in economics. The aim is to provide exposure to a wide variety of methods that may be used by graduate research students to simulate and solve economic models. The course will cover in some depth the specification and solution of dynamic stochastic models and of econometric estimation routines using a single computer package. Currently there are two packages and hence two sets of lectures/labs available to students. The first set covers Z-Tree whilst the other covers Python. After consulting with supervisors and PG Directors, students would choose lectures in the software that is most suited to their intended research. Students would be assessed via an assignment set by and graded by the School of Economics.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations		Other requirements	All first year PhD students in Economics will have the option to sit this course subject to the approval of the supervisor(s).

Course Delivery Information
Not being delivered

Learning Outcomes
On completion of this course, the student will be able to: Although exact topics covered will vary from year to year it is expected that after taking the course all students will be able to: Use practically all of the capabilities and functions of the relevant computational software being taught (currently Python or Z-Tree). Use the relevant software to effect simulation of a nonlinear dynamic stochastic economic model. (Students taking Z-Tree) Use the software to run experiments