Postgraduate Course: Computational Macroeconomics (ECNM11101)

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 course exposes students to some of the main tools used to numerically solve and analyse nonlinear dynamic stochastic general equilibrium models. These tools covered will focus on nonlinear solution methods and will be illustrated through application to economic models. By the end of the course students will have solved several canonical models in macroeconomics and they will have learnt the tools and developed the skills needed to solve many more.
Course description	The course will be divided into the following units: 1. Getting to know Julia. 2. Numerical differentiation: derivatives, gradients, Jacobians, hessians. a. Symbolic b. Finite differences c. Automatic differentiation 3. Optimization a. Newton¿s method b. Steepest descent c. Trust region method 4. Solving systems of nonlinear equations (unconstrained, box-constrained, mixed-complementarity problems/Kuhn-Tucker problems) a. Direct iteration b. Newton Raphson c. Levenberg-Marquardt 5. Function approximation a. Piecewise linear approximation b. Polynomial approximation c. Orthogonal polynomials 6. Numerical Integration a. Newton-Coates b. Quadrature c. Monte Carlo integration 7. Model-solving a. Time iteration (examples might be drawn from) i. Stochastic growth model (Brock-Mirmann) ii. Canonical new Keynesian model (Woodford/Gali) iii. Canonical labour search model (Gali) iv. Two-country international real business cycle model b. Value function iteration (examples might be drawn from) i. Stochastic growth model ii. Subsistence consumption iii. Epstein-Zin preferences

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations		Other requirements	None

Course Delivery Information

Academic year 2025/26, Not available to visiting students (SS1)		Quota: 0
Course Start	Block 4 (Sem 2)
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 18, Summative Assessment Hours 2, Revision Session Hours 1.5, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 76 )
Assessment (Further Info)	Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment)	Coursework assignment, 2000-2500 words, 100%
Feedback	The assignment will be marked 0-100 by a marker and then moderated to ensure consistent standards are applied across the course. The structure of the assignment is that students will have to independently write the necessary code to solve a model, carefully motivate and document their solution method, and then use the solution obtained to answer specific questions provided in the assignment. These questions will be specific and ask students to calculate statistics, produce tables and figures, interpret results and so forth. The code written to solve and analyse the model will be submitted as part of the assignment. When marking student answers, we are (roughly) looking at the following criteria: - Structure and format: Do the answers have a good structure and logical flow, is the formatting sensible, are citations in order? For example, are tables and figures clearly labelled and can the reader understand what is shown in a table or plotted in a figure without having to go through the supplied computer code? - Methodology, logic and formal correctness: Do the answers show an understanding of the course content in terms of the applied methodology, whether results, arguments and discussions are logically consistent and formally correct? This importantly includes whether the computer codes are correct and the results are correctly produced and replicable.
No Exam Information

Learning Outcomes
On completion of this course, the student will be able to: Demonstrate critical understanding of the strengths and limits of the principal computational methods for solving models. Apply a significant range of standard and specialized numerical methods. Use a range of specialised numerical methods to analyse macroeconomic models at the forefront of development. Critically review, conceptualise, and extend learned skills and practices to new and original models.