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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Events of the 1970s and 1980s have provoked intense controversy about the desirability of existing political and economic institutions. On the basis of an analysis of social welfare in varying types of market systems and in certain democratic political systems, Democracy and Markets illuminates alternative directions for institutional reform. Examining in detail the experiences of several democratic European countries, John R. Freeman considers whether a mixed ownership structure is preferable to a private ownership structure; and whether a pluralist type of democratic politics is preferable to a corporatist type. Freeman compares the benefits of the two economic and two political systems separately, and then analyzes the workings of four basic political economies. This analysis yields a welfare taxonomy for alternative forms of democratic capitalism and more specifically a characterization of the blends of collective gain and distributional equity that can be achieved in the four systems. Freeman demonstrates the validity of this taxonomy through an empirical investigation of the political economies of Britain, Austria, Sweden, and Italy. Under current conditions, he concludes, the corporatist-mixed system produces the most desirable blend of welfare outcomes.

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.