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Time Series Analysis for the Social Sciences
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  • Cited by 18
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

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    Enns, Peter K. Kelly, Nathan J. Masaki, Takaaki and Wohlfarth, Patrick C. 2016. Don’t jettison the general error correction model just yet: A practical guide to avoiding spurious regression with the GECM. Research & Politics, Vol. 3, Issue. 2, p. 205316801664334.

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    Aima, Khan and Zaheer, Abbas 2015. Portfolio balance approach: An empirical testing. Journal of Economics and International Finance, Vol. 7, Issue. 6, p. 137.

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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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