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 Econometrics for Finance I
Code ACFI225
Coordinator Dr A Gazi
Finance and Accounting
Adnan.Gazi@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2022-23 Level 5 FHEQ First Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
ACFI111 QUANTITATIVE METHODS FOR ACCOUNTING AND FINANCE 

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 24

 6

         30
Timetable (if known) 120 mins X 1 totaling 24
  		60 mins X 1 totaling 6
  		         
Private Study 120
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Assessment 1: Multiple Choice Midterm Assessment Type: Unseen Examination, Not Managed by SAS Duration: 1 hour Weighting: 30% Reassessment Opportunity: Yes Penalty for Late Submission: Standard U  1    30       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Assessment 2: Individual Report Assessment Type: Coursework Size: 2500 words Weighting: 60% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty Applies Anonymous Asse  0    60       
Assessment 3: Individual Presentation Assessment Type: Presentation Duration: 5 minutes Weighting: 10% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty applies Ano  5    10       

Aims

This module aims to:
Equip students with knowledge of financial econometrics with a particular emphasis on multivariate regression models.
Enables the students to develop their knowledge of financial econometrics and to develop their practical skills by using a statistical package to estimate econometric models.
Prepare students for their future research project.

Learning Outcomes

(LO1) Students will be able to apply their knowledge of regression models using the statistical software Eviews

(LO2) Students will be able to test single and multiple hypotheses

(LO3) Students will be able to interpret the output of a statistical software

(LO4) Students will be able to evaluate the assumptions underpinning the estimation methodology

(LO5) Students will be able to identify and address the various issues pertaining to a particular analysis

(S1) Problem Solving Skills

(S2) Analytical Skills

(S3) Communication Skills

(S4) Lifelong Learning

(S5) Digital Fluency

Teaching and Learning Strategies

The module entails 12 face to face lectures. The key applications will be mostly done during the 6 seminars (1-hour each) in the computer lab. Journal articles and financial press articles will be used to illustrate the real-world applications of the key concepts. The teaching will also be supported by online activities. These include self-assessment quizzes that enable students to evaluate their own learning and receive formative feedback instantaneously. Furthermore, discussion boards will be used to foster the exchange of ideas, thus deepening the knowledge of students and improving their communication skills. These activities will be moderated by the module instructor. Students will also be directed to key academic and practitioner readings to further develop their learning.

Teaching Method 1: Lecture
Scheduled Directed Student Hours: 24
Attendance Recorded: Yes

Teaching Method 2: Seminar
Description: The 1-hour seminars will take place every 2 weeks in the computer lab. During these sessions, students will apply their knowledge to practical problems and develop their expertise in Eviews, the statistical software.
Scheduled Directed Student Hours: 6
Attendance Recorded: Yes

Self-Directed Learning Hours: 120
Description: These independent learning hours are aimed at supporting the directed student learning. The module leader will provide guidance in the form of suggested readings, topics, or self-assessment quizzes to complete with the expectation that students are well prepared to contribute to the tutorial activities and to understand the content of lectures.

Skills and Attribute Mapping

Skill 1: Problem Solving Skills
How is it developed: By engaging with the seminar, lecture, and self-assessment activities, and by working on the coursework
How is it assessed (if applicable): Midterm, Presentation and Coursework

Skill 2: Analytical
How is it developed: In lectures, seminars, and by w orking on both the self-assessment quizzes and the coursework
How is it assessed (if applicable): Midterm, Presentation and Coursework

Skill 3: Communication Skills
How is it developed: In class discussions (lectures and seminars), by using the discussion board, and by preparing the coursework and associated video presentation
How is it assessed (if applicable): Presentation and Coursework

Skill 4: Lifelong Learning
How is it developed: In lectures and seminars by critically thinking about financial problems and empirically evaluating data to make informed decisions
How is it assessed (if applicable): Presentation and Coursework

Skill 5: Digital Fluency
How is it developed: In working with digital tools and specialist software to engage with the course materials, download, process, analyse data, and communicate
How is it assessed (if applicable): Midterm , Presentation and Coursework

Syllabus
 

• Multiple regression model
• Multiple hypothesis tests
• Diagnostic checks: Autocorrelation, multicollinearity, and heteroskedasticity
• Omitted variable bias
• Dummy variable and parameter stability

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