Module description
What is the module about?
This course is an intermediate econometrics module which will focus on the models required to analyse time series data. Students will gain a deep understanding of several types of time series modelling approaches. This will enable them to make real-world forecasts of important economic and financial series, useful for further study and careers in economics, finance, retail and others.
Who should do this module?
This course should be taken by students who have an interest in how to forecast and explain the way economic variables behave over time. These skills are sought-after in careers in central banks, policymakers, finance and others. The course will also be useful to students thinking of studying econometrics in the final year or at the postgraduate level. It will expose students to a mixture of problem-solving and data analysis and interpretation
Provisional Lecture Outline
Lecture 1: Introduction to Time Series
Lecture 2: Univariate Models – Autoregression Part 1
Lecture 3: Univariate Models – Autoregression Part 2
Lecture 4: Univariate Models – MA and ARMA
Lecture 5: Multivariate Models
Lecture 6: Multi-step Forecasting and Pseudo Out-of-Sample Methods
Lecture 7: Nonstationarity – Trends and Unit Roots
Lecture 8: Nonstationarity – Spurious Regression & Cointegration
Lecture 9: Nonstationarity – Seasonality and Breaks
Lecture 10: Volatility Models
You must have already covered the following at your home university to be able to be registered on this module:
Regression analysis, simple regression model; multiple regression analysis, estimation and inference; data scaling, beta coefficients, logarithmic functional forms, quadratics, interactions; multiple regressions with binary (or dummy) variables; probability models, panel data.
Assessment details
60% Project
40% Project
Teaching pattern
Weekly Lecture
Weekly Tutorial
Suggested reading list
Stock, J. and Watson, M. “Introduction to Econometrics” 3rd Ed., Pearson