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Instructor-led course

Provided by: Joint Schools' Social Sciences


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Module 10:Time Series Analysis
Prerequisites


Description

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.

Target audience

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

Prerequisites
  • 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.
Topics covered
  • Session 1: Introduction to Time Series
  • Session 2: Time Series Regression
  • Session 3: Smoothing
  • Session 4: Decomposition Methods
Objectives

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

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

Presentations, demonstrations and practicals

Assessement

Three exercises

Textbook(s)

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

Notes
  • 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
Duration

Four sessions of two hours

Frequency

Four times in Lent term

Theme
Advanced Statistics

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