University of Sheffield Department of Economics
ECN602
ECN602
Applied Macroeconometrics
Personnel
Module leader
Gurleen Popli
Contact details
Room 418, g.popli@shef.ac.uk
Consultation and Feedback Times: to be announced later
Other Lecturers
George Efthyvoulou
Contact details
Room 435, G.Efthyvoulou@sheffield.ac.uk
Consultation and Feedback Times: to be announced later
Tutors
Gurleen Popli, George Efthyvoulou
Prerequisites
ECN 6540
Students are strongly advised to have achieved a good grade in ECN 6540 since this
module requires a strong background in econometrics.
Module Aims and Objectives
The aims of this module is to give students:
-
an introduction to the econometric techniques that are appropriate to economic
modelling of macroeconomic and financial data.
practical experience of applying standard macro-econometric techniques for time
series data, with a focus upon interpretation of results.
The intended learning outcomes are that by the end of the module you should
demonstrate
-
critically evaluate empirical studies in macroeconomics and to appreciate some of
the problems associated with estimating time series data.
use appropriate statistical software (STATA) to address an applied research
problem.
produce basic original applied econometrics studies using time series
econometric techniques.
Transferable Skills
The skills that will be acquired/enhanced in the course of this module are:
-
data analysis and interpretation,
individual research,
report writing.
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University of Sheffield Department of Economics
ECN602
Timetable
Lectures:
There will be ten lectures over the semester, two hours each. Lectures will be held
on Wednesdays: 10:00-11:50 in HI LT7.
The purpose of lectures is to provide an introduction and overview to concepts and
techniques but should not be interpreted as a definitive coverage of all relevant
applications.
Computer labs:
There will be four computer labs over the semester, one hour each. Labs will be
held on Wednesdays: 14 to 14:50 in MAPP ME03. The weeks in which labs will be
held will be announced at the beginning of the semester.
Student Responsibilities
This is a 15 credit module and therefore it is expected it will require 150 study hours
per student (including formal teaching hours). As a rough guideline this means you
should expect to devote about 10 hours of study time per week to this module during
a 15 week semester.
Attendance at all lectures and computer labs is compulsory and will be monitored.
Computer lab exercises, whenever possible, will be placed on MOLE2 at least one
week prior to the lab session and you are expected to work through the exercises
prior to the lab, and to bring your answers to the lab.
Computer labs are NOT about giving out answers to the lab problems, but instead
will focus on discussing particular aspects of the material presented that students
have found difficult. Students should make use of staff consultation and feedback
times to discuss any issues they have with the module and which are not resolved in
labs.
Assessment
The assessment of this module will be by an assignment (100%).
Coursework questions will be available no later than Wednesday 13th March, week 6
Submission date will be Wednesday 15th May, week 12.
Further details of the nature of assignment will be given closer to the date.
Coursework must always be submitted in hard copy to the Enquiry Office, and by no
later than 3.00pm on the day of the deadline. Unauthorised late submissions will
incur a penalty of 5% of the mark per day for 5 working days and after that a mark of
zero. A Turnitin submission should be made within 5 working days of the paper copy
deadline. Failure to submit an assessment to Turnitin by this date will lead to a zero
mark for the assessment. Marked coursework will be returned within 3 working
weeks.
The pass mark for this module is 50%.
Any change to assessment arrangements will be announced in lectures and also via
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University of Sheffield Department of Economics
ECN602
the website.
Reassessment
In the case of students who fail the assessment of this module, repeat assessment
will be by assignment. Resit candidates must consult the website for further
information, up until the time of the reassessment, not merely in semester time.
Website
Students must read the module website regularly. The header on the homepage will
indicate new posts but students should also check regularly for announcements.
Where students are expected to have access to lecture notes in the lecture these will
always be posted at least 24 hours before the lecture and lab exercises, whenever
possible, at least one week before the relevant session.
For general questions about the module, or the material covered in class, students
are strongly encouraged to use the discussion forum on the module website. Emails
to the lecturers/tutors should only be used for personal queries related to the
module. Lecturers will check and respond, if necessary, to queries posted on the
discussion forum of the module.
Student Feedback
The module website has a Module Feedback Report which gives a detailed account
by the module leader of the student experience on this module last year. Soon after
coursework is returned to students, general feedback will be given on the website on
performance in the coursework, in addition to the individual feedback given on the
pink cover sheet which accompanies returned coursework
Course Outline
Recommended Books:
Enders, Walter. ‘Applied Econometric Time Series’. 3rd Edition. Wiley.
Verbeek, Marno. ‘A guide to modern econometrics’. 4th Edition. John Wiley and
Sons, Ltd.
Further readings will be given later.
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University of Sheffield Department of Economics
ECN602
List of Topics
Note that the schedule of topics may be altered.
Five weeks (Gurleen Popli)
1. Fundamental concepts: white noise processes, stationarity, autocovariance and
autocorrelation functions.
2. Stationary time series: autoregressive models (AR), moving average models
(MA), autoregressive moving average models (ARMA).
3. Model building: identification, estimation, diagnostic checking, model selection
criteria.
4. Forecasting: properties of optimal forecasts, computation of forecasts, updating
forecasts.
Five weeks (George Efthyvoulou)
5. Non-stationary time series: trends, unit roots, testing for unit roots, structural
change.
6. Time series models of heteroscedasticity: autoregressive and generalized
autoregressive conditional heteroscedasticity models (ARCH and GARCH).
7. Multivariate time series models: dynamic models with stationary variables,
models with non-stationary variables (spurious regressions, cointegration, errorcorrection mechanisms).
8. Multivariate time series models: vector autoregressive
cointegration in a VAR, testing for cointegration.
models
(VAR),
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