Time Series Analysis for the Social Sciences (Paperback)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Series:	Analytical Methods for Social Research
Release date:	December 2014
Authors:	Janet M. Box-Steffensmeier • John R. Freeman • Matthew P. Hitt • Jon C. W. Pevehouse
Dimensions:	226 x 152 x 23mm (L x W x T)
Format:	Paperback - Trade
Pages:	298
ISBN-13:	978-0-521-69155-0
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Social sciences > Sociology, social studies > Social research & statistics > General Books > Science & Mathematics > Mathematics > Applied mathematics > General
LSN:	0-521-69155-9
Barcode:	9780521691550

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