Age-Period-Cohort Models: Approaches and Analyses with Aggregate Data (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Age-Period-Cohort Models: Approaches and Analyses with Aggregate Data presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach. Abstract: Age-Period-Cohort Models: Approaches and Analyses with Aggregate Data presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach Develop a Deep Understanding of the Statistical Issues of APC Analysis AgePeriodCohort Approaches and Analyses with Aggregate Data presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach. See How Common Methods Are Related to Each Other After a general and wide-ranging introductory chapter, the book explains the identification problem from algebraic and geometric perspectives and discusses constrained regression. It then covers important strategies that provide information that does not directly depend on the constraints used to identify the APC model. The final chapter presents a specific empirical example showing that a combination of the approaches can make a compelling case for particular APC effects. Get Answers to Questions about the Relationships of Ages, Periods, and Cohorts to Important Substantive Variables This book incorporates several APC approaches into one resource, emphasizing both their geometry and algebra. This integrated presentation helps researchers effectively judge the strengths and weaknesses of the methods, which should lead to better future research and better interpretation of existing research. Introduction to the Age, Period, and Cohort MixIntroductionInterest in Age, Period, and CohortImportance of CohortsPlan for the BookMultiple Classification Models and Constrained RegressionIntroductionLinearly Coded Age-Period-Cohort (APC) ModelCategorically Coded APC ModelGeneralized Linear ModelsNull VectorModel FitSolution Is Orthogonal to the ConstraintExamining the Relationship between SolutionsDifferences between Constrained Solutions as Rotations of SolutionsSolutions Ignoring One or More of the Age, Period, or Cohort FactorsBias: Constrained Estimates and the Data Generating Parameters .. Develop a Deep Understanding of the Statistical Issues of APC AnalysisAge-Period-Cohort Models: Approaches and Analyses with Aggregate Data presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach. See How Common Methods Are Related to Each OtherAfter a general and wide-ranging introductory chapter, the book explain This book presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach. Emphasizing both the geometry and algebra of several APC approaches, the book develops readers' understanding of the statistical issues of APC analysis and shows how common methods are related to each other.