‘The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.’

- John Fox, Professor, Department of Sociology, McMaster University

‘The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.’

- Ben Jann, Executive Director, Institute of Sociology, University of Bern

‘Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.’

-Tom Smith, Senior Fellow, NORC, University of Chicago

Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities.

Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method's logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method's application, making this an ideal text for PhD students and researchers embarking on their own data analysis.

Chapter 17: Time-Series Cross-Section

Time-Series Cross-Section

Time-series cross-section


Social scientists have long investigated relationships between institutional, economic and social variables either by using comparisons between entities, such as schools, cities, regions, and countries, or through repeated observations in one geographic unit over time. Methodologies to examine these dimensions simultaneously, for instance time-series cross-section (TSCS) estimations, were only developed and applied in political science in the mid-1980s, and gained popularity with the publication of Stimson's (1985) seminal essay on the topic. Since then, the number of publications harnessing the strength of this technique has surged. The combination of cross-sections and time-series is a powerful analytical strategy to accommodate the interaction of the temporal and spatial dimensions in social science theories. It is therefore not surprising that TSCS models have become extremely ...

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