ULMS Electronic Module Catalogue
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Advanced Topics in Macroeconometrics
Code ULMR807
Coordinator Professor A Taamouti
Economics
Abderrahim.Taamouti@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2023-24 Level 8 FHEQ Second Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
 

Modules for which this module is a pre-requisite:
 

Programme(s) (including Year of Study) to which this module is available on a required basis:
 

Programme(s) (including Year of Study) to which this module is available on an optional basis:
 

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 18

           18
Timetable (if known)              
Private Study 132
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Individual project. Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: Yes  0    70       
Individual presentation. Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: No  30    30       

Aims

This module aims to:

1. Equip Ph.D. students with the tools necessary for state-of-the-art empirical research with economic time series data (macro-economics, finance, marketing, accounting...etc).

2. Lay out the econometric theory of time series analysis.

3. Inspire and motivate students by discussing recent research in this field.

Learning Outcomes

(LO1) Students will be able to build theoretical models to approach research questions.

(LO2) Students will be able to numerically compute simple models.

(LO3) Students will comprehend the links between Economic theory and Macroeconometric models

(LO4) Students will have a critical awareness of the latest approaches to macroeconometrics modelling.

(LO5) Students will be able to establish a suitable macroeconometric model for a specific question.

(LO6) Students will be able to critically evaluate research papers in macroeconometrics.

(LO7) Students will have critical awareness of recent empirical approaches in macroeconometrics.

(S1) Research skills.
Students will develop research skills during lectures and reading journal articles.

(S2) Problem solving.
Students will develop problem solving skills through their assessments.

(S3) IT skills.
Students will develop IT skills through learning programming of macroeconometrics techniques like estimation, inference, etc.

(S4) Numeracy.
Students will develop numeracy through computation in the project.

(S5) Organisation and ability to work under pressure.
Students will develop this skill during the assessments.

(S6) Presentation skills.
Students will develop presentation skills through the presentation assessment aspect of the module.

Teaching and Learning Strategies

Lectures x 18 hours (90 minutes per week).

During the lectures, the instructor will cover some of the topics listed in the syllabus. But students will also have to read published papers and working papers and will present a paper of their choice. This will form part of the module's assessment.

Self-directed learning x 132 hours
Students should make use of their self-directed learning time to read the module textbooks and journal articles, think about good ideas for their PhD dissertations.

Syllabus
 

Indicative Syllabus:

The module will cover a selection of the following topics:

1. Univariate Time Series Models;

2. Non-stationary linear models;

3. Stationary multivariate time series models;

4. Non-Stationary multivariate models; and if time allows

5. A selection of further topics (this will be specified at the start of the module), which might include some recent development on Granger causality analysis and quantile regression analysis.

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.