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  3. Time Series Analysis for the Social Sciences
  • Cited by 121
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    • Publisher:
      Cambridge University Press
      Publication date:
      05 December 2014
      22 December 2014
      ISBN:
      9781139025287
      9780521871167
      9780521691550
      DOI:
      https://doi.org/10.1017/CBO9781139025287
      Dimensions:
      (228 x 152 mm)
      Weight & Pages:
      0.52kg, 292 Pages
      Dimensions:
      (228 x 152 mm)
      Weight & Pages:
      0.39kg, 298 Pages
      • Subjects:
      Research Methods in Politics, Politics: General Interest, Politics and International Relations, Research Methods In Politics
      Series:
      Analytical Methods for Social Research
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    Subjects:
    Research Methods in Politics, Politics: General Interest, Politics and International Relations, Research Methods In Politics
    Series:
    Analytical Methods for Social Research
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    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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