Postgraduate

NBS8331 : Introductory Econometrics

Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0

Aims

The module seeks to:
- explain how econometrics have been used and can be used to test theories in economics and finance literature
- provide a comprehensive set of fundamental technical skills necessary to pursue empirical research in economics and finance
- offer the opportunity to develop some key quantitative skills that are highly valued in the private sector

The module covers essential material from probability theory, data description, hypothesis testing, and regression analysis with a particular focus on the analysis of cross-sectional data.

Outline Of Syllabus

1. Simple Regression Model: population versus sample regression function, the method of Ordinary Least Squares (OLS), assumptions of the classical linear regression model, obtaining and interpreting the OLS estimates, statistical properties of estimators, measures of fit
2. Classical Normal Linear Regression Model: hypothesis testing and confidence interval estimation.
3. Extensions of the linear regression model: regression through the origin, units of measurement, standardization, logarithmic and semi-logarithmic models.
4. Multiple Regression Model: restricted least squares, tests for multiple linear restrictions.
5. Dummy Variable Regression Models: describing qualitative information, qualitative regressors, analysis of variance (ANOVA) models, regression with a mixture of quantitative and qualitative regressors (ANCOVA models).
6. Relaxing the assumptions of the classical model – multicollinearity, non-normality, heteroskedasticity, and serial correlation: consequences, detection, and robust inferences in their presence.

Teaching Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture151:0015:00pip
Guided Independent StudyAssessment preparation and completion130:0030:00N/A
Guided Independent StudyDirected research and reading133:0033:00Readings from the books
Scheduled Learning And Teaching ActivitiesSmall group teaching31:003:00pip: Small group teaching (seminars)
Guided Independent StudyIndependent study119:0019:00N/A
Total100:00
Teaching Rationale And Relationship

-Lectures provide the fundamental technical structure of the methods introduced and an overview of the essential empirical economic and financial modelling methods
- Seminars provide an opportunity to enhance both theoretical and practical (computer-based) aspects of modelling

Assessment Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

The format of resits will be determined by the Board of Examiners

Exams
Description Length Semester When Set Percentage Comment
Written Examination901A100unseen exam
Exam Pairings
Module Code Module Title Semester Comment
LBS8331Introductory Econometrics1London equivalent module
Formative Assessments
Description Semester When Set Comment
Prob solv exercises1MGroup feedback on problem-based exercises in seminars and PC labs
Prob solv exercises1MSeminar questions provided
Prob solv exercises1MWeekly homework problem sets
Assessment Rationale And Relationship

The 90 mins examination is an appropriate way to assess the theoretical understanding and problem solving skills under the time constraint as required in industry.

The alternative if there is a lockdown is a PC examination that will meet the same goals.

Reading Lists

Timetable