Pooled time series analysis can improve the statistical efficiency of estimates -- vital for social science researchers with data available from both temporal observations at regular intervals (time series) and from observations at single points of time (cross-sections). By `pooling' time series and cross-sectional data, researchers can increase the sample size and do a more effective analysis.This volume covers a variety of pooled time series models including the constant coefficients model in which the parameters are constant across space and time, the least squares dummy variable model which permits the intercept to vary by time and by cross-section, the error components model which takes explicit account of cross-sectional and time series disturbances, and the structural equation model, which goes beyond the error components model.

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