Time Series Analysis

MATH38032 Time Series Analysis 10 Semester 2 Undergraduate Level 3 Wentao Li Unit coordinator Department of Mathematics 1 FHEQ level (Framework for Higher Education Qualifications) ' Last part of a Bachelors ' European Credit Transfer & Accumulation System Rating : 5.0 This course unit covers a variety of concepts and models useful for empirical analysis of time series data. This course unit covers a variety of concepts and models useful for empirical analysis of time series data. To introduce the basic concepts of the analysis of time series, with emphasis on financial and economic data. On successful completion of this course unit students will be able to:  <ul> <li> Explain the concepts and general properties of stationary and integrated univariate time series.</li> <li> Explain the concepts of linear filter and linear prediction, and derive best linear predictors for time series.</li> <li> Apply the backwards shift operator and the concept of roots of the characteristic equation to the study of time series models.</li> <li> Explain the concepts of autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and seasonal autoregressive integrated moving average (seasonal ARIMA) time series, and derive basic properties thereof.</li> <li> Apply the basic methodology of identification, estimation, diagnostic checking and model selection to time series model building.</li> <li> Explain some basic concepts in the analysis of multivariate time series - multivariate autoregressive model, joint stationarity and cointegration.</li> </ul> <ul> <li> Introduction and examples of economic and financial time series, asset returns. Basic models: white noise, random walk, AR(1), MA(1). [2]</li> <li> Stationary time series. Autocovariance and autocorrelation functions. Linear Prediction. Yule-Walker equations.  Estimation of autocorrelation and partial autocorrelation functions. [3]</li> <li> 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]</li> <li> 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]</li> <li> Filtering, exponential smoothing, seasonal adjustments. [2]</li> <li> Multivariate time series. Stationarity, autocorrelation and crosscorrelation. Multivariate autoregressive model.  Markov property.  Representation of univariate autoregressive models in Markov form. [3]</li> <li> Model based forecasting from ARMA and ARIMA. [3]</li> </ul> Three lectures and one examples class each week. In addition students should expect to spend at least six hours each week on private study for this course unit. 1 Written exam 80% 2 Written assignment (inc essay) 20% 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. MATH27720 Probability and Statistics 2 Pre-Requisite Compulsory MATH27711 Linear Regression Models Pre-Requisite Compulsory MATH30832 Pre-Requisites: MATH27720 and MATH27711Students 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) N (core) Cryer, Jonathan D and Chan, Kung-Sik. Time Series Analysis with Applications in R.  Second edition. Springer, 2008. (or a newer edition) (essential) Cowpertwait, Paul SP and Metcalfe, Andrew V. Introductory Time Series with R. Springer, 2009. (recommended) Shumway, Robert H and Stoffer, David S. Time Series Analysis and Its Application: With R Examples. Second edition. Springer, 2006. (further reading) Mills, Terence C. The Econometric Modelling of Financial Time Series. Second edition. Cambridge University Press, 1999. Lectures 12 Tutorials 12 0 66