title: Mixed models, linear dependency, and identification in age-period-cohort models.
abstract: This paper examines the identification problem in age-period-cohort models that use either linear or categorically coded ages, periods, and cohorts or combinations of these parameterizations. These models are not identified using the traditional fixed effect regression model approach because of a linear dependency between the ages, periods, and cohorts. However, these models can be identified if the researcher introduces a single just identifying constraint on the model coefficients. The problem with such constraints is that the results can differ substantially depending on the constraint chosen. Somewhat surprisingly, age-period-cohort models that specify one or more of ages and/or periods and/or cohorts as random effects are identified. This is the case without introducing an additional constraint. I label this identification as statistical model identification and show how statistical model identification comes about in mixed models and why which effects are treated as fixed and which are treated as random can substantially change the estimates of the age, period, and cohort effects. Copyright © 2017 John Wiley & Sons, Ltd.
authors.affiliation: University of Oregon, Eugene, Oregon, 97403, U.S.A.
authors.forename: Robert M
authors.identifier: http://orcid.org/0000-0002-8269-2906
authors.initials: RM
authors.lastname: O'Brien
mesh_terms: D000367:Age Factors; D056808:Biostatistics; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D015233:Models, Statistical
keywords: hierarchical age-period-cohort models; identification in age-period-cohort models; mixed age-period-cohort models; statistical model identification