Robert O'Brien

Age-Period-Cohort Models (eBook, ePUB)

Approaches and Analyses with Aggregate Data

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Erscheinungstermin: 19. August 2014
• Englisch
• ISBN-13: 9781040054901
• Artikelnr.: 72285170

Autorenporträt

Robert M. O'Brien is a professor emeritus of sociology at the University of Oregon. He specializes in criminology and quantitative methods and has published extensively in both areas. In what is labeled semiretirement, he coedits Sociological Perspectives with James R. Elliott and Jean Stockard and works on research projects.

Inhaltsangabe

Introduction to the Age, Period, and Cohort Mix. Multiple Classification
Models and Constrained Regression. Geometry of APC Models and Constrained
Estimation. Estimable Functions Approach. Partitioning the Variance in APC
Models. Factor-Characteristic Approach. Conclusions: An Empirical Example.
Index.

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Introduction to the Age, Period, and Cohort Mix. Multiple Classification Models and Constrained Regression. Geometry of APC Models and Constrained Estimation. Estimable Functions Approach. Partitioning the Variance in APC Models. Factor-Characteristic Approach. Conclusions: An Empirical Example. Index.

Rezensionen

"This is an excellent book on age-period-cohort (APC) models for the analysis of data from demography and related fields. ... an excellent work with wonderful resources on APC models. ... It will be very useful to research scholars ... looking for several advanced APC approaches. The book effectively presents the strengths and weaknesses of various statistical methods of APC analysis, which should lead to advanced future research and better interpretation of existing research."
-International Statistical Review, 2015