
Academic Year:  2017/8 
Owning Department/School:  Department of Mathematical Sciences 
Credits:  6 [equivalent to 12 CATS credits] 
Notional Study Hours:  120 
Level:  Honours (FHEQ level 6) 
Period: 

Assessment Summary:  CW 25%, EX 75% 
Assessment Detail: 

Supplementary Assessment: 

Requisites:  Before taking this module you must take MA20227 
Description:  Aims: To introduce a variety of statistical models for time series, cover the main methods for analysis and give practical experience in fitting such models. Learning Outcomes: At the end of the course, the student should be able to: * compute and interpret a correlogram and a sample spectrum; * derive the properties of ARIMA and statespace models; * choose an appropriate ARIMA model for a given set of data and fit the model using R; * compute forecasts for a variety of linear methods and models. Skills: Numeracy T/F A Problem Solving T/F A Written and Spoken Communication F Content: Introduction: Examples, simple descriptive techniques, trend, seasonality, the correlogram. Probability models for time series: Stationarity; moving average (MA), autoregressive (AR), ARMA and ARIMA models. Estimating the autocorrelation function and fitting ARIMA models. Forecasting: Exponential smoothing, Forecasting from ARIMA models. Stationary processes in the frequency domain: The spectral density function, the periodogram, spectral analysis. Statespace models: Dynamic linear models and the Kalman filter. 
Programme availability: 
MA30085 is Optional on the following programmes:Department of Economics

Notes:
