Time Series Analysis for Political and Social Data (join a waiting list)
Date:
20/06/2017 - 21/06/2017
Organised by:
NCRM, University of Southampton
Presenter:
Professor Will Jennings
Level:
Entry (no or almost no prior knowledge)
Contact:
Jacqui Thorp
Training and Capacity Building Co-ordinator
NCRM, University of Southampton
Phone: 02380 594069
Email: jmh6@soton.ac.uk
Map:
View in Google Maps (SO17 1BJ)
Venue:
Building 39, University of Southampton, Highfield, Southampton, Hants
Description:
This course provides an introduction to time series methods and their application to social science research. Many of our theoretical questions in the social sciences are implicitly temporal in nature – such as whether a change in public policy leads to a change in social behaviour, whether that relates to recycling, voting or offending, and the widespread availability of social and political longitudinal data make it possible to address them. There are specific issues associated with time series data – due to their temporal structure and dependence – that requires careful attention. This course will introduce participants to the time serial structure of social and political data, fundamental concepts in time series analysis, diagnostic tests for different time series processes (i.e. stationarity and unit root), and static and dynamic regression models (including “ARIMA”, autoregressive distributed lag and error-correction models) for social and political variables.
The course covers:
- The structure of political and social time series.
- Fundamental concepts in time series analysis.
- Diagnostic tests for autocorrelation, moving average and stationary/integrated processes.
- Univariate and static/dynamic regression models.
By the end of the course participants will:
- Have developed an understanding of the theoretical structure of time series data, and be able to organise their own data in this format.
- Be able to apply diagnostic tests for time series processes to their own data.
- Be able to select the appropriate model for univariate and multivariate specifications, and estimate and interpret the short- and long-run effects of variables, lag distributions and rates of ‘error-correction’.
The target audience for this event is academics or government researchers from the social sciences (not including economics) with some background in quantitative methods in general, but no experience of time series analysis specifically. This may range from PhD students to more advanced researchers looking for an introduction to a new method.
Some knowledge of linear regression models is assumed but prior training in time series analysis is not required or expected. The course will make use of basic algebra. The computer workshop will use Stata. Some familiarity with Stata would be helpful but for those without preparatory materials will be provided ahead of the course and the teaching materials will provide a crash course during the session.
Participants are requested to bring one or more social/political time series that are relevant to your research (e.g. crime rates, survey data on vote intentions or support for government cuts, social trust indicators).
Preparatory Reading
Paul Kellstedt and Guy Whitten. (2013). The Fundamentals of Political Science Research. Cambridge University Press. Chapter 11.
Mark Pickup. (2014). Introduction to Time Series Analysis. Sage.
Janet M. Box-Steffensmeier, John R. Freeman, Jon C. W. Pevehouse and Matthew Perry Hitt. (2014). Time Series Analysis for the Social Sciences. Cambridge University Press.
Cost:
The fee per teaching day is:
• £30 per day for UK/EU registered students
• £60 per day for staff at UK/EU academic institutions, UK/EU Research Councils researchers, UK/EU public sector staff and staff at UK/EU registered charity organisations and recognised UK/EU research institutions.
• £220 per day for all other participants
All fees include event materials, lunch, morning and afternoon tea. They do not include travel and accommodation costs.
Website and registration:
Region:
South West
Keywords:
Time Series Analysis, Time Series Data , Univariate Regression Models , Dynamic Regression Models
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