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SSST Subjects

EC 285 Advanced Econometrics

 
 

Programme(s) where module is offered

  • BSc in Economics with International Business
  • BSc in Economics with Finance
 

Status (core, option, free choice)

Core

 

FHEQ Level

6

 

Unit Value

8 ECTS

 

Semester taught

Autumn

 

Pre-Requisite Modules or Qualifications

EC281 Introduction to Econometrics

 

Module Code

EC 285

 

Module coordinator

Amer Demirovic

 

Applicable From

2017

 

Educational Aims of the Module

  • This module aims to develop a further understanding of the use of regression analysis and related techniques for quantifying economic relationships and testing economic theories.
  • With the Introduction to Econometrics being the foundation, this module moves on to more advanced econometrics methods listed in the module outline. It is tailored to deepen the students’ knowledge of econometrics and give them the ability to conduct their own empirical research.
  • The module also covers the other, more advanced types of data, such as Panel and Time Series in order to fully prepare the students for their future academic and practical research and work.
 

Module Outline/Syllabus

  • Review econometrics used in published papers
  • Panel data models
  • Correlation of errors in panel data models
  • Binary/ordinal variables as dependent variables (Probit /Logit models)
  • Instrumental variables
  • Stationarity
  • Random Walk model
  • Autoregressive Moving Average (ARMA) models
  • Autoregressive conditional heteroscedasticity (ARCH) models
  • Maximum Likelihood estimation
  • Vector autoregressive models (VAR)
  • Vector error correction models (VEC)
 

Student Engagement Hours

Type Number per Term Duration Total Time

Lectures

10 1.5 15.0

Workshops

10 1.5 15.0

Seminars

5 1.5 7.5
Tutorials 5 1.5 7.5

Total Contact Hours

45.0

Total Guided/Independent Learning Hours

155.0
Total Engagement Hours 200.0
 

Assessment Method Summary

Type Number Required Duration / Length Weighting Timing / Submission Deadline

Exam

1

3 hours

50%

Week 17

Mid-term Exam

1

2 hours

20%

Week 8

Open-book Computer based Practical Test (IT Lab)

2

2x1 hour

2x15%

Weeks 4, 12

 

Module Outcomes

Intended Learning Outcomes:

  • To comprehend key terminology from the field of Advanced Econometrics.

  • Conceptual understanding of the theory underlying advanced econometrics models

  • Ability to critically analyse functional relationships between variables in advanced econometrics models such as logit, GARCH, ARMA, panel data, VAR and VEC.

  • Ability to critically evaluate models’ assumptions and consequences of violation of these assumptions

  • Understanding of limits of econometrics models

  • Ability to critically analyse relationships between variables outside the context studied in the class

  • Ability to independently design and complete a research project

  • To be able to practically apply theoretical knowledge, recognise problems and apply learned methods and techniques for creating solutions

  • Use already established methods to critically analyze significance, reliability and validity of results.

Teaching and Learning Strategy:

  • Lectures (ILO: 1-9)

  • In-class case studies (ILO: 1-9)

  • Tutorials will provide a platform for in depth analysis and topical discussion (ILO: 1-9)

  • Computer lab exercises will enable students to learn how to use an econometrics software (Eviews) to practically estimate models and interpret the results on their own. (ILO: 3-9)

Assessment Strategy:

  • Midterm-semester test and practical subject tests (ILO: 1-5)

  • Final exam (ILO: 6,8,9)

  • Computer based test (ILO: 3,4,7,8,9)

Practical Skills:

  • Ability to conduct own empirical research

  • To use a number of econometric methods for quantifying economic relationships and testing economic theories using econometrics software (Eviews)

  • Skill to use econometrics software (Eviews) to practically estimate models and interpret the results

Teaching and Learning Strategy:

  • Analyses of published empirical studies in lectures and tutorials (PS:2)

  • Practical computer lab sessions (PS: 1-3)

Assessment Strategy:

  • Computer based test (PS: 2-3)

  • Practical tests (PS: 1)

Transferable Skills:

  • Research Skills

  • Numerical Skills

  • Communication Skills: Written and Oral

  • Classify data

  • Compare, inspect or record facts

  • IT Skills

  • Meet deadlines

  • Good time management

  • Ability to work independently

Teaching and Learning Strategy:

  • Tutorials (TS: 2-6)

  • Computer lab sessions (TS: 1-6)

  • Practical tests (TS: 1-9)

Assessment Strategy:

  • Computer based test (TS:1-6)

  • Practical tests (TS: 1-9)

  • Midterm exam (TS: 2-5)

  • Final exam (TS: 2-5)

 

Key Texts and/or other learning materials

Set text

  • Gujarati, D. (2014), Econometrics by Example, 2 Edition, Palgrave Macmillan

Supplementary Materials

  • A.H. Studenmund, (2017) Using Econometrics a practical guide, 7 Edition, Pearson

  • Stock, J., Watson, M., (2014), Introduction to Econometrics, 3 Edition, Pearson

  • Verbeek, M., (2012), A Guide to Modern Econometrics, 4 Edition, John Wiley & Sons

  • Green, William H., (2012) Econometric Analysis, Pearson

  • Elsevier (2017) Journal of Econometrics [online] http://www.sciencedirect.com/science/journal/03044076/196/2/open-access (Accessed 12 April 2017)

  • Enders, Walter, (2014) Applied Econometric Time Series, 4th Edition, Wiley

Please note

This specification provides a concise summary of the main features of the module and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the learning opportunities that are provided.

More detailed information on the learning outcomes, content and teaching, learning and assessment methods of each module and programme can be found in the departmental or programme handbook.

The accuracy of the information contained in this document is reviewed annually by the University of Buckingham and may be checked by the Quality Assurance Agency.

Date of Production : April 2017

Date approved by School Learning and Teaching Committee

Date approved by School Board of Study

Date approved by University Learning and Teaching Committee

Date of Annual Review

       
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