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Unit of study_
QBUS3850: Time Series and Forecasting
Time series and dynamic modelling is a fundamental component of modern business practice. Further, forecasting is a required component of business decision making. This unit provides an introduction to the time series models used for the analysis of data arising in different business areas including finance, accounting, marketing, economics and many other disciplines. It then considers methods for point and interval forecasting, testing and sensitivity analyses, in the context of these models. Topics include: the properties of time-series data; Seasonal Exponential smoothing and ARIMA models; Vector Autoregressions; modelling and forecasting conditional volatility, via ARCH and GARCH; forecasting risk measures such as Value at Risk and Expected Shortfall; dynamic factor models. Emphasis is placed on applications involving the analysis of many real business datasets. Students are encouraged to undertake hands-on analysis using appropriate software.
| Code | QBUS3850 |
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| Academic unit | Business Analytics |
| Credit points | 6 |
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Prerequisites:
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QBUS2820 |
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Corequisites:
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None |
| Prohibitions:
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None |
At the completion of this unit, you should be able to:
- LO1. understand the characteristics of time-series data in order to analyse real business data of this form
- LO2. select and use an appropriate technique to predict the future behaviour of business variables of interest
- LO3. design and estimate of a range of quantitative, statistical models for volatility and risk
- LO4. appraise the suitability of both models and methods for forecasting business and financial data
- LO5. develop complex programs in Python software for estimation of time series models and forecasting
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