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Online course materials for MATH38032

Time Series Analysis


Unit code: MATH38032
Credit Rating: 10
Unit level: Level 3
Teaching period(s): Semester 2
Offered by School of Mathematics
Available as a free choice unit?: N

Requisites

Prerequisite

Additional Requirements

MATH38032 pre-requisites

Students are not permitted to take more than one of MATH38032 or MATH48032 for credit in the same or different undergraduate year.

 

Students are not permitted to take MATH48032 and MATH68032 for credit in an undergraduate programme and then a postgraduate programme.

 

Note that MATH68032 is an example of an enhanced level 3 module as it includes all the material from MATH38032

 

When a student has taken level 3 modules which are enhanced to produce level 6 modules on an MSc programme taken within the School of Mathematics, then they are limited to a maximum of two such modules (with no alternative arrangements available otherwise)

Aims

To introduce the basic concepts of the analysis of time series, with emphasis on financial and economic data.

Overview

This course unit covers a variety of concepts and models useful for empirical analysis of time series data.

Learning outcomes

On successful completion of this course unit students will

  • have understanding of the basic time series concepts;
  • be able to build models to time series data and critically assess them using a variety of methods for exploration of time series data, identification and model selection.
  • Future topics requiring this course unit

None

Assessment Further Information

End of semester examination: two hours weighting 100%

Syllabus

  • Introduction and examples of economic and financial time series, asset returns. Basic models: white noise, random walk, AR(1), MA(1). [2]
  • Stationary time series. Autocovariance and autocorrelation functions. Linear Prediction. Yule-Walker equations. Estimation of autocorrelation and partial autocorrelation functions. [3]
  • Models for stationary time series - autoregressive (AR) models, moving average (MA) models, autoregressive moving average (ARMA) models. Seasonal ARMA models. Properties, estimation and model building. Diagnostic checking. [6]
  • Non-stationary time series. Non-stationarity in variance - logarithmic and power transformations. Non-stationarity in mean. Determinisitic trends. Integrated time series. ARIMA and seasonal ARIMA models. Modelling seasonality and trend with ARIMA models. [4]
  • Filtering, exponential smoothing, seasonal adjustments. [2]
  • Non-linear models - threshold AR, bilinear models. Cointegration. [2]
  • Multivariate time series. Stationarity, autocorrelation and crosscorrelation. Multivariate autoregressive model. Markov property. Representation of univariate autoregressive models in Markov form. [3]

Recommended reading

  • Cryer, Jonathan D and Chan, Kung-Sik. Time Series Analysis with Applications in R. Second edition. Springer, 2008.
  • Mills, Terence C. The Econometric Modelling of Financial Time Series. Second edition. Cambridge University Press, 1999.
  • Shumway, Robert H and Stoffer, David S. Time Series Analysis and Its Application: With R Examples. Second edition. Springer, 2006.
  • Cowpertwait, Paul SP and Metcalfe, Andrew V. Introductory Time Series with R. Springer, 2009.

Feedback methods

Feedback tutorials will provide an opportunity for students' work to be discussed and provide feedback on their understanding.  Coursework or in-class tests (where applicable) also provide an opportunity for students to receive feedback.  Students can also get feedback on their understanding directly from the lecturer, for example during the lecturer's office hour.

Study hours

  • Lectures - 22 hours
  • Tutorials - 11 hours
  • Independent study hours - 67 hours

Teaching staff

Georgi Boshnakov - Unit coordinator

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