Unit information: Applied Financial Econometrics in 2023/24

Unit name	Applied Financial Econometrics
Unit code	ECONM0009
Credit points	20
Level of study	M/7
Teaching block(s)	Teaching Block 2 (weeks 13 - 24)
Unit director	Dr. Khatoon
Open unit status	Not open
Units you must take before you take this one (pre-requisite units)	Econometrics with Python
Units you must take alongside this one (co-requisite units)	None
Units you may not take alongside this one	None
School/department	School of Economics
Faculty	Faculty of Social Sciences and Law

Unit Information

Why is this unit important?

This unit aims to deliver the knowledge and understanding of the key time series econometric methodologies in an applied fashion. This will be particularly useful for those who would like to work on macroeconomics and financial economics sector. The complex theories are blended with applications using data and software packages to achieve in depth understanding. The unit is aimed enhance student ability to understand empirical time series literature, and to replicate and possibly extend them. Students will learn both predictive and causal econometric methods of time series data in a comparative manner and will be able to choose among different methods depending on practical goals.

How does this unit fit into your programme of study?

This unit is one of the three technical options under set 1 for this program. The content is built on the Econometrics training that you will receive in TB1, and will extend it to time series econometrics applications to help undertake overtime information and perform both short run and long run analysis of economic and financial variables.

Your learning on this unit

An overview of content

• Fundamental concepts: Stationary stochastic process, autocovariance and autocorrelation functions
• Modelling univariate time series under stationarity: Autoregressive models (AR), Moving Average models (MA), Autoregressive Moving Average models (ARMA)
• Modelling volatility and correlation: Autoregressive, Generalized Autoregressive Conditional Heteroscedasticity (ARCH and GARCH) models
• Multivariate Time series analysis: Vector Autoregression (VAR), Vector Error Correction (VEC) models
• Advanced topics: Structural Break and Threshold models

How will students, personally, be different as a result of the unit

You will learn the fundamental concepts of time series Econometrics via hands on exercises. The unit introduces additional econometric software packages in a comparative manner with options for you to choose your preferred one. The content will help you to identify the different challenges that the time dimension can bring to econometric analysis and how to choose appropriate method to handle those challenges.

Learning Outcomes

1. Develop a firm understanding of econometric methodologies used to analyse macroeconomic and financial data
2. Recognize which method should be applied in different contexts of time series analysis
3. Critically evaluate published empirical research to analyse the strengths and weaknesses in such work
4. Conduct time series analysis using appropriate software packages and ability to writeup the results in a formal fashion
5. Prepare a video presentation to explain the findings of the time series analysis

How you will learn

The unit will be taught using a combination of

• pre-lecture asynchronous material (comprising of videos, interactive games, and reading), and large group interactive lectures (to meet learning outcomes 1, 2, and 3), and
• weekly small group lab sessions (to meet learning outcomes 2 and 4)

The applied nature of the unit involves a lot of data analysis work, which will be supported by weekly formative problem sets followed by peer dialogue. These problem sets can be considered as broken down parts of the summative coursework. The formative peer dialogue will also help you to prepare for the summative video presentation by providing training on communication skills.

How you will be assessed

Tasks which help you learn and prepare you for summative tasks (formative):

Weekly formative tasks followed by peer dialogue

Tasks which count towards your unit mark (summative):

Coursework in the form of an empirical project (approximately 1500-2000 words)(80%) (ILOs 1, 2, 3, 4) 

Video presentation based on the coursework (20%) (ILO 5)

When assessment does not go to plan

Reassessment will be through a (re)submission of the coursework, and the video presentation.  

Resources

If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. ECONM0009).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the University Workload statement relating to this unit for more information.

Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates within the Regulations and Code of Practice for Taught Programmes.