Time Series Analysis for the Social Sciences
$29.00 ( ) USD
- Janet M. Box-Steffensmeier, Ohio State University
- John R. Freeman, University of Minnesota
- Matthew P. Hitt, Louisiana State University
- Jon C. W. Pevehouse, University of Wisconsin, Madison
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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 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.Read more
- Appendix on difference equations to introduce students to the mathematics behind time series methods
- Highly accessible discussion of the methodology of time series analysis using numerous and widely varied examples in the social sciences
- Includes an online companion website with code and examples as well as a guide to software for time series analysis
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- Date Published: November 2014
- format: Adobe eBook Reader
- isbn: 9781316055762
- contains: 93 b/w illus. 30 tables
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
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.
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