Time Series Analysis for the Social Sciences

This book provides instruction and examples of the core methods in time series econometrics, drawing from several main fields of the social sciences.

Janet M. Box-Steffensmeier (Author), John R. Freeman (Author), Matthew P. Hitt (Author), Jon C. W. Pevehouse (Author)

9780521871167, Cambridge University Press

Hardback, published 22 December 2014

292 pages, 93 b/w illus. 30 tables
23.6 x 15.6 x 2.1 cm, 0.52 kg

Time series, or longitudinal, data are ubiquitous in the social sciences. Unfortunately, analysts often treat the time series properties of their data as a nuisance rather than a substantively meaningful dynamic process to be modeled and interpreted. Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. Janet M. Box-Steffensmeier, John R. Freeman, Jon C. Pevehouse and Matthew P. Hitt cover a wide range of topics including ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting. This book is aimed at researchers and graduate students who have taken at least one course in multivariate regression. Examples are drawn from several areas of social science, including political behavior, elections, international conflict, criminology, and comparative political economy.

1. Modeling social dynamics
2. Univariate time series models
3. Dynamic regression models
4. Modeling the dynamics of social systems
5. Univariate, nonstationary processes: tests and modeling
6. Co-integration and error-correction models
7. Selections on time series analysis
8. Concluding thoughts for the time series analyst.

Subject Areas: Research methods: general [GPS]