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  3. Time Series Analysis for the Social Sciences
  • Cited by 114
Publisher:
Cambridge University Press
Online publication date:
December 2014
Print publication year:
2014
Online ISBN:
9781139025287
DOI:
https://doi.org/10.1017/CBO9781139025287
Subjects:
Research Methods in Politics, Politics: General Interest, Politics and International Relations, Research Methods In Politics
Series:
Analytical Methods for Social Research
Information

Book description

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.

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