Skip to main content

Trinity College Dublin, The University of Dublin

Trinity Menu Trinity Search



You are here Postgraduate > Diploma in Applied Economics and Big Data > Course Structure > Computational Methods for Economics

Computational Methods for Economics

Module Code: ECP77584, ECP77594, ECP88234

  • ECTS Credit: 5
  • Mandatory/Optional: Optional
  • Semester/Term Taught: Hilary Term
  • Module Coordinator: Professor Joseph Kopecky

Module Learning Aims

This module provides students with an introduction to the use of computational methods for solving economic problems. Part of the module will focus on properly setting up an appropriate environment to best leverage modern methods while producing reproduceable and reusable code. We will then study various numerical methods that are commonly used in economic modelling and apply them to specific problems.

Module Learning Outcomes

Upon completing this module students should:

  • be comfortable setting up an environment to solve complex economic models using modern methods;
  • Produce reproducible code;
  • Understand best practices as well as good habits for carrying out computational economic modelling;
  • Be familiar with the concepts behind core numerical solution methods, and their use in common economic problems.

Module Content

Computational methods are used across every field of economics. This module serves as an introduction to some commonly used methods in Computational Economics. The primary aim is to equip students with the fundamentals needed for a wide range of applications. We will begin with some basics about programming fundamentals and setting up a python environment. We will then study some important numerical methods used in quantitative economics and put them to use simulating economic models.

Reading List

https://quantecon.org/

Module Pre-Requisite and Module Co-Requisite

None

Assessment Details

For PhD and Diploma students:

  • 50% continuous assessment
  • 50% project

For MSc students

  • 50% continuous assessment
  • 50% exam

Module Website

Blackboard