Literature DB >> 23701919

The impossibility of separating age, period and cohort effects.

Andrew Bell1, Kelvyn Jones.   

Abstract

This commentary discusses the age-period-cohort identification problem. It shows that, despite a plethora of proposed solutions in the literature, no model is able to solve the identification problem because the identification problem is inherent to the real-world processes being modelled. As such, we cast doubt on the conclusions of a number of papers, including one presented here (Page, Milner, Morrell, & Taylor, 2013). We conclude with some recommendations for those wanting to model age, period and cohort in a compelling way.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Keywords:  Age–period–cohort models; Collinearity; Model identification

Mesh:

Year:  2013        PMID: 23701919     DOI: 10.1016/j.socscimed.2013.04.029

Source DB:  PubMed          Journal:  Soc Sci Med        ISSN: 0277-9536            Impact factor:   4.634


  26 in total

1.  COHORT CHANGES IN THE SOCIAL DISTRIBUTION OF TOLERANT SEXUAL ATTITUDES.

Authors:  Fred C Pampel
Journal:  Soc Forces       Date:  2016-11-07

2.  Attitudes Toward Computers Across Adulthood From 1994 to 2013.

Authors:  Chin Chin Lee; Sara J Czaja; Jerad H Moxley; Joseph Sharit; Walter R Boot; Neil Charness; Wendy A Rogers
Journal:  Gerontologist       Date:  2019-01-09

3.  Childhood Socioeconomic Status and Late-Adulthood Mental Health: Results From the Survey on Health, Ageing and Retirement in Europe.

Authors:  Viola Angelini; Daniel D H Howdon; Jochen O Mierau
Journal:  J Gerontol B Psychol Sci Soc Sci       Date:  2019-01-01       Impact factor: 4.077

4.  Should age-period-cohort studies return to the methodologies of the 1970s?

Authors:  Eric N Reither; Ryan K Masters; Yang Claire Yang; Daniel A Powers; Hui Zheng; Kenneth C Land
Journal:  Soc Sci Med       Date:  2015-01-13       Impact factor: 4.634

5.  Temporal Trends in the Remaining Lifetime Risk of Cardiovascular Disease Among Middle-Aged Adults Across 6 Decades: The Framingham Study.

Authors:  Ramachandran S Vasan; Danielle M Enserro; Vanessa Xanthakis; Alexa S Beiser; Sudha Seshadri
Journal:  Circulation       Date:  2022-04-18       Impact factor: 39.918

6.  Testing Persistence of Cohort Effects in the Epidemiology of Suicide: an Age-Period-Cohort Hysteresis Model.

Authors:  Louis Chauvel; Anja K Leist; Valentina Ponomarenko
Journal:  PLoS One       Date:  2016-07-21       Impact factor: 3.240

7.  Rising trends in intrahepatic cholangiocarcinoma incidence and mortality: getting at the root cause.

Authors:  Mustafa Raoof; Gagandeep Singh
Journal:  Hepatobiliary Surg Nutr       Date:  2019-06       Impact factor: 7.293

8.  Differences in adiposity trajectories by birth cohort and childhood social class: evidence from cohorts born in the 1930s, 1950s and 1970s in the west of Scotland.

Authors:  Richard J Shaw; Michael J Green; Frank Popham; Michaela Benzeval
Journal:  J Epidemiol Community Health       Date:  2014-02-06       Impact factor: 3.710

9.  Suicide in Sri Lanka 1975-2012: age, period and cohort analysis of police and hospital data.

Authors:  Duleeka W Knipe; Chris Metcalfe; Ravindra Fernando; Melissa Pearson; Flemming Konradsen; Michael Eddleston; David Gunnell
Journal:  BMC Public Health       Date:  2014-08-13       Impact factor: 3.295

10.  Future declines of coronary heart disease mortality in England and Wales could counter the burden of population ageing.

Authors:  Maria Guzman Castillo; Duncan O S Gillespie; Kirk Allen; Piotr Bandosz; Volker Schmid; Simon Capewell; Martin O'Flaherty
Journal:  PLoS One       Date:  2014-06-11       Impact factor: 3.240

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