Time Series Analysis for the Social Sciences

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.