Module 10:Time Series Analysis

Prerequisites

This module is part of the Social Science Research Methods Course 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.

The module introduces time series techniques relevant to forecasting in social science research and computer implementation of the methods.

Mphil Students from participating departments taking the Social Science Research Methods Course as part of their research degree

• Students need a background in basic statistical theory and working knowldge of SPSS
• Students strongly recommended to complete Module 4 Linear Regression before attending this module.

Number of sessions: 4

• Session 1: Introduction to Time Series
• Session 2: Time Series Regression
• Session 3: Smoothing
• Session 4: Decomposition Methods

The objective is to understand moving average; exponential smoothing and decomposition

• To learn time series techniques relevant to forecasting in social science research and computer implementation of methods.

Presentations, demonstrations and practicals

Three exercises

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

• To gain the 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

Four sessions of two hours

Four times in Lent term

• Advanced Statistics
• JISC Digital Proficiency
• JISC Digital creation, problem solving and innovation