STAT40700

This course provides an opportunity for students to learn some basic techniques in Time Series Analysis. A Time Series is a set of measurements taken at regular intervals over a period of time. Time series pervade the worlds of economics and finance e.g. equity prices, (CPI) inflation, GNP, GDP, derivative prices, oil prices, etc. Although the techniques of Time Series Analysis are used by many people, from meteorologists to astronomers, this course will mainly focus on examples in the world of economics and finance. This course will show how to model such data and how using those models forecasts and predictions can be made.Time series analysis is not a new subject, however there have been many advances in recent years and the 2003 Nobel prize in Economics was awarded to Engle and Granger for their work in time-series econometrics. This course will cover both traditional methods and more modern approaches to Time Series Analysis. Upon completion of this course students should have mastered techniques that are extremely valuable for careers in the analysis of economic and financial data.

Topics covered include, among others:

- Introduction and basic concepts
- Autoregressive Moving Average (ARMA) models: definition and estimation of parameters
- Forecasting
- Autoregressive Integrated Moving Average (ARIMA) models: Identification and diagnostic checking
- Multivariate time series
- Cointegration

If time permits, we will also cover

- Generalised AutoRegressive Conditional Heteroskedasticity (GARCH) models
- Count time series modelling