Bounding Analyses of Age-Period-Cohort Effects.
For more than a century, researchers from a wide range of disciplines have sought to estimate the unique contributions of age, period, and cohort (APC) effects on a variety of outcomes. A key obstacle to these efforts is the linear dependence among the three time scales. Various methods have been pr...
| Publicado en: | Demography (Springer Nature) Vol. 56; no. 5; pp. 1975 - 2005 |
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| Autores principales: | , |
| Formato: | journal article |
| Publicado: |
Springer Nature
Oct2019
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=139186343&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 139186343 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00703370 DEM jtl: Demography (Springer Nature) issn: 00703370 maglogo: N pubinfo: dt: Oct2019 vid: 56 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 139186343 10.1007/s13524-019-00801-6 ppf: 1975 ppct: 30 formats: fmt: – @attributes: type: T – @attributes: type: P size: 3.4MB tig: atl: Bounding Analyses of Age-Period-Cohort Effects. aug: au: Fosse, Ethan Winship, Christopher affil: Department of Sociology, University of Toronto, M5S 2J4, Toronto, ON, Canada Department of Sociology, Harvard University, 02138, Cambridge, MA, USA su: Cohort analysis Homicide rates Homicide Age distribution Epidemiology Mathematical bounds Linear dependence (Mathematics) Prostate cancer Time Statistical models Prostate tumors sug: subj: Cohort analysis Homicide rates Homicide Age distribution Epidemiology Mathematical bounds Linear dependence (Mathematics) Prostate cancer Time Statistical models Prostate tumors keyword: Age-period-cohort (APC) models Bounding analysis Causal inference Identification problem Age-period-cohort (APC) models Bounding analysis Causal inference Identification problem ab: For more than a century, researchers from a wide range of disciplines have sought to estimate the unique contributions of age, period, and cohort (APC) effects on a variety of outcomes. A key obstacle to these efforts is the linear dependence among the three time scales. Various methods have been proposed to address this issue, but they have suffered from either ad hoc assumptions or extreme sensitivity to small differences in model specification. After briefly reviewing past work, we outline a new approach for identifying temporal effects in population-level data. Fundamental to our framework is the recognition that it is only the slopes of an APC model that are unidentified, not the nonlinearities or particular combinations of the linear effects. One can thus use constraints implied by the data along with explicit theoretical claims to bound one or more of the APC effects. Bounds on these parameters may be nearly as informative as point estimates, even with relatively weak assumptions. To demonstrate the usefulness of our approach, we examine temporal effects in prostate cancer incidence and homicide rates. We conclude with a discussion of guidelines for further research on APC effects. pubtype: Academic Journal doctype: journal article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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