A selection of panel studies appearing in the and the between 1990 and 2003 shows that sociologists have been slow to capitalize on the advantages of panel data for controlling unobservables that threaten causal inference in observational studies. This review emphasizes regression methods that capitalize on the strengths of panel data for consistently estimating causal parameters in models for metric outcomes when measured explanatory variables are correlated with unit-specific unobservables. Both static and dynamic models are treated. Among the major subjects are fixed versus random effects methods, Hausman tests, Hausman-Taylor models, and instrumental variables methods, including Arrelano-Bond and Anderson-Hsaio estimation for models with lagged endogenous variables.


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  • Article Type: Review Article
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