INTERMEDIATE ECONOMETRICS - 2017/8
Module code: ECO2010
SRISUMA S Dr (Economics)
Number of Credits
FHEQ Level 5
Module cap (Maximum number of students)
Overall student workload
Independent Study Hours: 118
Lecture Hours: 20
Laboratory Hours: 10
|Assessment type||Unit of assessment||Weighting|
|School-timetabled exam/test||1 HOUR LAB TEST (10 COMPULSORY QUESTIONS)||15%|
|School-timetabled exam/test||1 HOUR MCQ TEST (30 COMPULSORY QUESTIONS)||15%|
|Examination||2 HOUR EXAMINATION - 5 COMPULSORY WRITTEN QUESTIONS||70%|
Prerequisites / Co-requisites
ECO2047 (Introductory Econometrics) is a pre-requisite for this module
This module follows on from Introductory Econometrics and considers econometric theory and methods when Gauss Markov assumptions fail to hold. The first half of this module introduces different examples of the endogeneity problem and their solutions. The second half deals with stationary and nonstationary time series.
Introduce students to the techniques relevant for the estimation in the presence of endogenous variables and of econometric time-series models.
An important emphasis of the course is to give students with ‘hands-on' learning experience of econometric analysis using a variety of economic data sets along side the theory. For this purpose, a number of datasets will be made available to undertake econometric analysis using the EViews software package.
|Understand various forms of the endogeneity problem and the solutions that can be used to overcome it. This includes methods involving instrumental variables, estimation of simultaneous equations and simple panel data models.||KCPT|
|Interpret econometric models with a variety of functional forms including those with lagged independent and dependent variables.||KCPT|
|Understand a number of concepts relating to OLS estimation with time series data.||KCPT|
|Apply econometric techniques using E-views and interpret the output obtained.||KCPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Indicative content includes:
Instrumental variables, two stage least squares
Panel data model with fixed effects
Introduction to time series methods, Distributed lag models, Autocorrelation
Lag dependent variable models, Non-stationary time series
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Develop skills in analysing economic data in more realistic situations where Gauss Markov assumptions do not hold
Appreciate the complexities of econometric analysis, understanding importance and intuition behind various estimation strategies and tests
The learning and teaching methods include:
2 hour lecture per week x 11 weeks
1 hour lab session / tutorials per week x 10 weeks
The assessment strategy is designed to provide students with the opportunity to demonstrate:
Their understanding of basic econometric methods, and ability to apply these techniques to analyse time series data and linear models with endogeneity that may arise from omitted variables, unobserved heterogeneity or simultaneous equations.
Thus, the summative assessment for this module consists of:
Two coursework assessments. Each is worth 15% of the final mark consisting of:
1 Hour Lab test based on Eviews and
1 hour MCQ test.
Final exam of two hours of written exams, which consists of five compulsory questions covering all 11 weeks.
Formative assessment and feedback
Students receive verbal feedback during lectures and tutorials through direct questioning (in which multiple questions and real-world examples of the use of economics are discussed). In addition to this, they receive guideline solutions to tutorial questions, against which they can compare their own results. After the test feedback is provided for all individual questions.
Reading list for INTERMEDIATE ECONOMETRICS : http://aspire.surrey.ac.uk/modules/eco2010
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.