Econometrics & Data Analytics


This course is designed to give students an understanding of the basic tools for the statistical and econometric analysis of financial data. A good grounding in Statistics will enable students to develop empirical tests and estimate econometric models that can be used, for instance, in asset pricing, forecasting and risk estimation. The course has an applied emphasis and so the students will be given a good grounding on how to use econometric software to conduct formal statistical analysis of real-world problems.

The course will make it possible for participants:

  • To acquire an in-depth and practical understanding of the basic tools for empirical modelling and statistical inference in Markets.
  • To assess critically the current state-of-the-art of empirical research in a range of topics.
  • To develop the practical skills required to carry out research in Financial Markets using standard econometric software.
  • To be able to apply for positions, for instance, in the research and portfolio management units of companies, financial institutions and organizations.

Learning Outcomes

On completing the course the participants will be able to:

  • Understand the basic principles for the statistical analysis of economic/financial data.
  • Understand the mechanics of hypothesis testing in the context of markets.
  • Develop empirical models that capture the stylized behaviour of data.
  • Use standard econometric software to undertake empirical research.
  • Assess critically other empirical work given the framework developed in the course.
  • Familiarize themselves with the fundamental principles of econometrics.
  • Apply financial and investment decision criteria in a variety of business cases.
  • Utilize valuation concepts as applied to shares and bonds.
  • Use models and their applications in relation to investment and business decisions.


  • Hypothesis Testing, Specification Testing, Dummy Variables
  • Estimation Methods and Inference
  • Simple and Multiple Regression
  • Introduction to Time Series Analysis
  • Univariate ARMA models, Autocorrelation and partial autocorrelation function
  • Granger causality tests
  • Special Topics: Nonstationary Variables (Cointegration) & Volatility (ARCH models)
  • Introduction to Data Analytics: Basic Concepts & Examples