This module builds upon earlier modules in microeconomic and macroeconomic theory with a view to introducing you to topics that both advance their understanding of the foundations of economic analysis and have applications across a range of social science and management disciplines, and developing their criticality.  The module will be looking at contemporary applications and will try to include employer participation.

At the micro-level, the module will include:

• Market power and pricing strategies

• Investment, time and insurance

• Risk and uncertainty analysis

• Asymmetric information

• Behavioural and experimental economics

At the macro-level, the module will include:

• Trade-off between inflation and unemployment

• Dynamic aggregate supply and demand

• Understanding consumer behaviour

• The theory of investment

• Stabilization policies

• Government debt and budget deficits

• Macroeconomics and the global financial crisis

Applied Econometrics develops advanced quantitative skills for analysing economic and financial data, with a particular emphasis on financial technology (fintech), digital finance, and modern empirical research methods. The module combines econometric theory with practical applications, enabling you to evaluate contemporary issues in financial economics using real-world data and rigorous causal inference techniques.

The module explores how econometric methods can be used to investigate key questions in fintech and financial markets, including digital payments, mobile banking, peer-to-peer lending, financial inclusion, digital currencies, platform finance, artificial intelligence in finance, financial innovation, credit markets, and the economic impacts of emerging financial technologies. Through these applications, you will learn how empirical evidence can be used to inform business strategy, financial regulation, and public policy.

A central feature of the module is the identification of causal relationships rather than simple statistical associations. You will be introduced to modern causal inference methodologies widely used in economics and finance, including panel data models, fixed-effects estimation, instrumental variables, difference-in-differences, regression discontinuity designs, matching methods, and event-study approaches. The module emphasises the assumptions underlying each identification strategy and develops your ability to critically evaluate empirical research and interpret causal evidence.

Alongside methodological training, you will develop practical skills in managing and analysing large-scale datasets using professional statistical software. They will learn data preparation, model estimation, diagnostic testing, interpretation of empirical results, and effective communication of quantitative findings through applied research projects. By integrating contemporary fintech applications with state-of-the-art econometric techniques, the module equips you with the analytical and empirical skills required for careers in finance, fintech, consulting, policy analysis, and economic research.

This module builds upon earlier modules in programming, data analytics, and foundational finance with a view to introducing you to cutting-edge tech topics that both advance their understanding of modern financial systems and have direct applications across digital platforms, quantitative analysis, and tech-driven decision-making. The module will focus heavily on contemporary, practical applications of AI and data science, and will actively try to include industry and employer participation.

The module will include:

• Deep Learning for Intelligent Prediction – Utilising advanced techniques to forecast large-scale business trends and market movements.

• Data Mining and Structural Pattern Recognition – Identifying hidden regimes and structural shifts within massive business datasets.

• Algorithmic Dynamic Modelling for Digital Economies – Simulating, testing, and building systems for fast-evolving digital markets and web-based platforms.

• Reinforcement Learning in Decision-Making – Training intelligent AI agents to execute optimal strategies and automated financial choices.

• Vibe Coding and Quantitative Behavioural Analysis – Using rapid, LLM-assisted development (vibe coding) to track, model, and capitalise on market sentiment and user behaviour.

This module is going to focus on primary, secondary and mixed data and how using data analysis can support decision making. It will equip students with essential skills to analyse, interpret, and present data effectively in professional settings. Equip students with the relevant practical skills through the application of advanced Ms Excel techniques and SPSS usage, to analyse variables to address decisions. Provide students with the critical skills required to collect and analyse data; and its presentation by means of relevant methods. 

Data - databases (such as FAME, Yahoo Finance and Investing.com), types of financial data with emphasis on big data, ethics in data handling, annual reports and other source. Data in SPSS - Entering data into SPSS, measuring and setting variables. 

Data mining - Data Visualisation Techniques in Excel-creation of dashboards using pivot tables, lookups and logical statements, use financial ratios to conduct performance analysis of companies with data on available databases.

Data Collection - Exploring techniques and tools for primary data collection and methods to analyse for understanding and presentation. Transferring and exporting collected data into Excel and SPSS for data analysis. 

Financial Statistics in Excel - use the 'Analysis ToolPak' -  and SPSS, descriptive statistics (mean, median, mode, standard deviation and variance), Anova (single factor, 'with and without' replication), correlations, regressions, T-Test, and Excel solver (for linear programming).

Financial Markets - market sensitivity (beta estimation), portfolio diversification (variance and covariance matrix), market reaction to announcement (event study-single and multiple events), market efficiency (use time series models such auto-regression and moving average concepts).

The module is designed to enable students to develop and apply business research and analytical skills that will enhance their employability and rapid progression to management positions in the workplace. It requires them to: 

• Undertake a research project that is 'scoped and framed' within a required degree programme of study.
• Design and conduct appropriate in-depth research in an elected area of study.
• Acquire, consolidate and apply theoretical knowledge, methodologies, and research approaches in a ‘real world’ environment.
• Develop and utilise skills in critical investigation; analysis and synthesis of evidence; reflection and autonomous learning.