EC976: Econometrics
Han Zhang
Module Leader
Juliana Cunha Carneiro Pinto
Module Lecturer
Principal Aims
EC976-15 Econometrics for MSc Finance EconomicsPrincipal Learning Outcomes
Subject knowledge and understanding By the end of the module the students will have a deeper and broader knowledge of material needed for empirical quantitative analysis The teaching and learning methods that enable students to achieve this learning outcome are: Series of lectures and tutorials The summative assessment methods that measure the achievement of this learning outcome are: Examination and written assignment.
Cognitive Skills Develop critical insight to appraise econometric results obtained by other researchers. Develop that habit of thought, knowledge and understanding to be able to carry out good quality applied econometric research with confidence and authority. The teaching and learning methods that enable students to achieve this learning outcome are: Class discussions, lectures, topic specific readings. Tutorial discussions and readings of journal articles. Data collection and replication of results. The summative assessment methods that measure the achievement of this learning outcome are: Examination and written assignment.
Key skills Developed key skills through class discussions, weekly exercises and tutorials. Have a deeper and broader knowledge and understanding of material needed for empirical quantitative analysis. The teaching and learning methods that enable students to achieve this learning outcome are: Series of lectures and tutorials.Series of lectures and tutorials The summative assessment methods that measure the achievement of this learning outcome are: Examination and written assignment.
Syllabus
The module will typically cover the following topics:
Part 1 focuses on microeconometrics and covers: Introduction to correlation vs. causation; OLS, estimation and inference. Dealing with unobservable characteristics; randomised control trials; instrumental variables; regression discontinuity design; differences-in-differences.
Part 2 focuses on macro and financial time series econometrics and covers: univariate time series models: autoregressive moving average models (ARMA). Model selection, diagnostic tests and forecasting. Dynamic models with stationary variables: distributed lag models and autoregressive distributed lag models (DL and ADL). Nonstationary variables: trends and unit root processes. Cointegration analysis and error correction models.
Context
- Core Module
- LN1J - Year 1
Assessment
- Assessment Method
- Coursework (20%) + 2 hour exam (January) (80%)
- Coursework Details
- 2 hour exam (January) (80%) , Assessment 1 (20%)
- Exam Timing
- January