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Macroeconomics (ECON0107)

Key information

Faculty
Faculty of Social and Historical Sciences
Teaching department
Economics
Credit value
15
Restrictions
N/A
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

The purpose of this course is to teach students to think about macroeconomic issues in an explicitly dynamic framework.

In part I (methods) we provide a rigorous introduction to dynamic problems in economics that combines the tools of dynamic programming with numerical techniques. The aim is to offer an integrated framework for studying applied problems in macroeconomics. We first review the formal theory of dynamic optimization; we then present the numerical tools necessary to evaluate the theoretical models. We then cover growth, real business cycle models, consumption theory and asset pricing. In tutorial classes, students are introduced to Matlab which is used to solve and simulate the economic models. In part II (topics) we cover a set of major topics in macroeconomics, including the study of stochastic growth, optimal monetary policy, consumption theory with endogenous incomplete markets, and the role of institutional features on dynamic choices.

Objectives:

At the end of the course, students should:

  • Understand the formulation of dynamic recursive economic models in a way allowing application to empirical data
  • Understand the numerical methods used to solve these models
  • Be able to actually implement these models on concrete economic problems
  • Master some of the most relevant dynamic models in macroeconomics, such as dynamic general equilibrium models of business cycle, models of consumption and of optimal taxation, and be familiar with the related literature

Module deliveries for 2024/25 academic year

Intended teaching term: Terms 1 and 2 ÌýÌýÌý Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
In person
Methods of assessment
100% Exam
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
34
Module leader
Professor Morten Ravn

Last updated

This module description was last updated on 8th April 2024.

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