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Constraints in Random Effects Age-Period-Cohort Models

Bibliographic Data

ID6169979
AuthorsLiying Luo (0000-0001-5393-6695, Department of Sociology and Criminology, Population Research Center, Pennsylvania State University, University Park, PA, USA, corresponding author), James S Hodges (0000-0001-7467-6941, University of Minnesota)
Year2020
Volume50
Issue1
Pages276-317
Publication date2020-02-11
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSociological Methodology (JOURNAL)
Journal identifiersISSN: 0081-1750 • E-ISSN: 1467-9531
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/0081175020903348
PMID36644415
OpenAlexW3006616641
LanguageEN
Citations received23
References cited38

Random effects (RE) models have been widely used to study the contextual effects of structures such as neighborhoods or schools. The RE approach has recently been applied to age-period-cohort (APC) models that are unidentified because the predictors are exactly linearly dependent. However, research has not fully explained how the RE specification identifies these otherwise unidentified APC models. We address this challenge by first making explicit that RE-APC models have greater—not less—rank deficiency than the traditional fixed-effects model, followed by two empirical examples. We then provide intuition and a mathematical proof to explain that for APC models with one RE, treating one effect as an RE is equivalent to constraining the estimates of that effect’s linear component and the random intercept to be zero. For APC models with two REs, the effective constraints implied by the model depend on the true (i.e., in the data-generating mechanism) nonlinear components of the effects that are modeled as REs, so that the estimated linear components of the REs are determined by the true nonlinear components of those effects. In conclusion, RE-APC models impose arbitrary although highly obscure constraints and thus do not differ qualitatively from other constrained APC estimators

Cohort · Cohort effect · Econometrics · Period (music · Physics · Sociology · Statistics · demographic modeling and climate adaptation · Demography · Global Health Care Issues · Insurance, Mortality, Demography, Risk Management · Mathematics · Psychology

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Unique citing works23
Citations per year3,83
Citation span2020 - 2026 (7)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 22

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