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 ECONOMETRIC AND STATISTICAL METHODS
Code ECON814
Coordinator Dr GD Liu-Evans
Economics
Gareth.Liu-Evans@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 7 FHEQ First 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 12

6

      12

6

36
Timetable (if known) 60 mins X 1 totaling 12
 
60 mins X 1 totaling 6
 
      60 mins X 1 totaling 12
60 mins X 1 totaling 6
 
 
Private Study 114
TOTAL HOURS 150

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Examination There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When): Semester 1  24 hours    100       
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Mid-term test There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When): Semester 1  1 hour         

Aims

The aim of this module is to give the student an understanding of basic econometric and statistical methods suitable for financial and economic data series. Extensive use will be made of econometrics software including EViews in tutorials to supplement the theory with applications and to provide hands-on experience. The aims are that the student will:

Understand the multiple regression model including the matrix and statistical background;

Be apply to apply statistical tests estimate regression models;

Understand the assumptions and limitations;

Understand the maximum likelihood principle and be able to perform the relevant specification tests;

Understand the principle underlying instrumental variables and GMM estimation;

Be confident in the use of econometric software such as EViews for a range of methods and applications.


Learning Outcomes

(LO1) Formulate and estimate regression models.

(LO2) Perform diagnostics on regression models.

(LO3) Perform all the calculations required via EVIEWS.

(LO4) Perform maximum likelihood estimation and be aware of the properties of the estimators.

(LO5) Perform GMM estimation.

(S1) Problem solving skills

(S2) Numeracy

(S3) IT skills

(S4) Communication skills


Teaching and Learning Strategies

Hybrid delivery, with social distancing on campus.

1 hour online asynchronous learning per week x 12 weeks
1 hour online synchronous lecture per week x 12 weeks
1 hour face-to-face seminar every other week x 6 weeks
1 hour face-to-face peer-to-peer learning every other week (unscheduled) x 6 weeks
Self-directed learning x 114 hours


Syllabus

 

Statistical and matrix background;

Inference in multiple regression models

Endogeneity;

Instrumental variables and generalised method of moments;

Maximum likelihood estimation and specification tests;

Introduction to asymptotics.


Recommended Texts

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