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 METHODS OF ECONOMIC INVESTIGATION 1: TIME SERIES ECONOMETRICS
Code ECON311
Coordinator Dr R Bu
Economics
Ruijunbu@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2022-23 Level 6 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 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: Mid-term test Assessment Type: Written Exam Duration: 1 hour Weighting: 20% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymou  1    20       
Assessment 2: Written Unseen Examination Assessment Type: Written Exam Duration: 2 hours Weighting: 80% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty app  2    80       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             

Aims

The aim of this module is to give students an understanding of econometric time series methodology. The module will build upon the materials of ECON212 Basic Econometrics. Important extensions include volatility models of financial time series and multivariate (multiple equations) models such as vector error correction and related cointegrating error correction models.

Learning Outcomes

(LO1) Specify and demonstratethe distributional characteristics of a range of time series models

(LO2) Estimate appropriate models for financial andeconomic time series for the purposes of forecasting andinference

(LO3) Understand a range ofunivariate and multivariate models of financial and economic time series processes.

(LO4) Applyunivariate and multivariate model selection and evaluation methods

(LO5) Understand theimplications of conditional heteroskedasticity, unit roots and    cointegration in economic andfinancial time series analysis

(S1) Problem solving skills

(S2) Numeracy

Teaching and Learning Strategies

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

Teaching Method: Seminar
Description: Seminars will provide students with the opportunity to further develop their skills through the exploration of various theoretical and practical problems, illustrated via actual data sets and real world problems of various sorts, both national and international in character.
Scheduled Directed Student Hours: 6
Attendance Recorded: Yes

Self-Directed Learning Hours: 120

Costs Information:
Students will occur no additional costs as a result of taking this module

There are the following non-modular requirements:
ECON212 preferably with a mark of 65%, or equivalent

Skills/Other Attributes Mapping

Skills / attributes: Problem solving skills
How this is developed: This is developed by a collection of suitably chosen practical questions and tasks covered and explained in the fortnight workshops.Mode of assessment (if applicable): Examination and Mid-term test

Skills / attributes: Numeracy
How this is developed: This is developed by a collection of suitably chosen theoretical questions covered and explained in the fortnight workshops.
Mode of assessment (if applicable): Examination and Mid-term test

Syllabus
 

Univariate Time Series Models
Introduction to Time Series Analysis
General ARMA Processes
Stationarity and Unit Roots
Testing for Unit Roots
Estimation of ARMA models
Model Selection
Predicting with ARMA Models
Autoregressive Conditional Heteroskedasticity

Multivariate Time Series Models
Dynamic Models with Stationary Variables
Models with Nonstationary Variables
Vector Autoregressive Models
Cointegration: the Multivariate Case

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