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 RESEARCH METHODS AND MODELLING
Code ECON815
Coordinator Dr Y Li
Economics
Yuyi.Li@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2019-20 Level 7 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

     10

     34
Timetable (if known)              
Private Study 116
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) :2  2 hours    80       
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) :2  1 hour    20       

Aims

The aim of this module is to give the student an understanding of econometric and statistical methods for financial and economic time series. Important extensions include: stationary models for time series; testing for nonstationarity; models for nonstationary data; volatility models for financial time series; and models for multivariate data including vector autoregressions, error correction mechanisms and cointegration.

Extensive use will be made of the econometrics package EViews in tutorials to supplement the theory with applications and to provide hands-on experience.

The aims are that the student will:

Understand a range of univariate and multivariate models of financial and economic time series and their applications;

Understand the statistical characteristics of financial data including skewness, kurtosis and volatility;

Understand the motivations, methods and limitations of multivariate modelling for economic and financial time-series;

Be confident in the use of an econometric computer programme (EViews) for a range of methods and applications.

Learning Outcomes

(LO1) Be able to specify and appreciate the main characteristics of a range of time series models;

(LO2) Be able to estimate appropriate models of financial and economic time series for purposes of forecasting and inference;

(LO3) Be able to apply univariate and multivariate model selection and evaluation methods;

(LO4) Be able to accommodate seasonality, causality, unit roots, and long run relationships in economic and financial time series;

(LO5) Be able to analytically investigate the skewness, kurtosis and volatility aspects of models such as ARIMA and ARCH models of economic and financial time series.

(S1) Communication skills

(S2) Planning and organisation skills

(S3) IT skills

(S4) Lifelong learning

Teaching and Learning Strategies

Teaching Method 1 - Lecture
Description:
Attendance Recorded: No

Teaching Method 2 - Laboratory Work
Description: Laboratory work involves the use of Management School data facilities such as the Bloomberg training centre where students are asked to apply conceptual work using real data.
Attendance Recorded: Yes

Syllabus
 

Univariate time series models;

Stationary models for time series;

Estimation and testing of ARIMA models;

Choosing a model;

Prediction with ARIMA models;

Unit roots and nonstationarity;

Testing for unit roots and random walks;

Autoregressive conditional heteroskedasticity;

Applications and examples;

Multivariate time series models;

Dynamic models with stationary variables;

Models with nonstationary variables;

Vector autoregressive models;

Vector error correction models;

Modelling cointegrated variables;

Applications and examples;

Software and data sources: Eviews, Bloomberg, Datastream, etc.

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