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2018/19 Taught Postgraduate Module Catalogue

TRAN5113M Transport Econometrics

15 creditsClass Size: 50

Module manager: Philip Wheat
Email: P.E.Wheat@its.leeds.ac.uk

Taught: Semester 1 View Timetable

Year running 2018/19

Pre-requisite qualifications

Prior economics experience required; discuss with tutor in advance.

Module replaces

LUBS5104M Quantitative Research Methods (as a compulsory for the MA Transport Economics programme; it remains as part of LUBS suite of modules)

This module is approved as an Elective

Module summary

This course will give the student the opportunity learn through a hands-on approach the use of econometrics in transport applications. After an introduction to the basic theory, which does require a need for matrix algebra, the lectures will make use of transport examples to demonstrate the various topics covered by the syllabus. The students will be provided with data to analyse in practicals and as coursework.

Objectives

On completion of this module, the student should have a basic understanding of the classical regression model and its applications in transport. This involves model specification, tests for validity of assumptions, methods for dealing with violation of assumptions and hypothesis testing. The student will be able to use econometric software to solve problems relevant to transport

Learning outcomes
The student will be able to interpret and critically assess the results of econometric analysis and to apply appropriate econometric methods to transport problems. The student will become familiar with the econometric software EVIEWS and be able to use the program to analyse transport data. In addition, the course will provide practice in the reporting and presentation of econometric results.


Syllabus

The topics covered are:
- Statistical background: inference, hypothesis testing;
- Ordinary Least Squares estimation, BLUE, assumptions, violation of assumptions;
- Multicolinearity; misspecification; omitted variables;
- Non-linearities; functional form;
- Cross-section data; time - series data; dynamic models; pooled and panel data; Stochastic Frontier Models.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Lecture111.0011.00
Practical111.0011.00
Private study hours128.00
Total Contact hours22.00
Total hours (100hr per 10 credits)150.00

Private study

Background reading for lectures; practice with econometric software; preparation of reports on practicals; coursework.

Opportunities for Formative Feedback

Attendance at lectures, practicals and tutorials; contributions to tutorials; assessed practicals and course work.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
Project2,500 word report50.00
Oral PresentationProject Presentation20.00
Computer ExerciseComputer exercise30.00
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 12/12/2018 16:33:10

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