Aims and Fit of Module

This module introduces students to model-building as a basis for analyzing time-series data, and to the use of formal, mainly quantitative, methods for improving decision-making in complex and uncertain situations. It emphasizes the use and interpretation of techniques rather than mechanistic details. By using complete R code examples throughout, this module provides a practical foundation for performing statistical forecasting and decision-making. It equips students with more advanced forecasting and decision-making models.

Learning outcomes

A	Demonstrate a critical understanding of advanced econometric models in time-seriesB	Formulate, estimate and conduct tests of hypotheses using time series data
C	Conduct forecasts in time series models and build appropriate models of volatility
D	Demonstrate an understanding of a variety of approaches to model-building and decision support in the context of biology
E	Apply analytical models/methods for statistic forecasting and decision-making in practical scenarios，understand how they work, and be able to implement them within a programming language

Method of teaching and learning

Course content will be delivered primarily via standard lectures that will be accompanied by suitable lecture handouts (also available on LMO). Students will also be guided to sections of specific textbooks and if reading of specific reviews or source literature is required, then copies of these will be made available to the students. Tutorials will be given as a platform to address any specific question or issue from individual students.