Time Series Analysis (Intensive)

Prerequisites

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This module introduces the time series techniques relevant to forecasting in social science research and computer implementation of the methods. Background in basic statistical theory and regression methods is assumed. Topics covered include time series regression, moving average, exponential smoothing and decomposition. The study of applied work is emphasized in this non-specialist module.

• Mphil and PhD students from participating departments taking the Social Science Research Methods Centre training programme as part of their research degree

• A background in basic statistical theory and regression methods
• A working knowledge of statistical concepts up to the level of Linear Regression
• You must have a University Information Services (Computing) Desktop Services password (http://www.ucs.cam.ac.uk/linkpages/newcomers)
• You must have access to the associated Moodle course page (http://www.ssrmc.group.cam.ac.uk/ssrmc-modules/before-first-session)

Number of sessions: 2

• Introduction to Time Series
• Time Series Regression
• Smoothing Moving average
• Decomposition Methods

Presentations, demonstrations and practicals

• One online exercise [optional, dependent upon department]
• The SSRMC encourages all students to take the assessment

Bowerman, B.L. O'Connell, R. & Koehler, A (2004). Forecasting Time Series and Regression (4th ed). Duxbury Press

• To gain maximum benefits from the course it is important that students do not see this course in isolation from the other MPhil courses or research training they are taking.
• Responsibility lies with each student to consider the potential for their own research using methods common in fields of the social sciences that may seem remote. Ideally this task will be facilitated by integration of the SSRMC with discipline-specific courses in their departments and through reading and discussion.

• This is an intensive, one-day module