Decomposition Method Based on a Model of Continuous Change.
A demographic measure is often expressed as a deterministic or stochastic function of multiple variables (covariates), and a general problem (the decomposition problem) is to assess contributions of individual covariates to a difference in the demographic measure (dependent variable) between two pop...
| Publicado en: | Demography Vol. 45; no. 4; pp. 785 - 802 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Science & Business Media B.V.
November 2008
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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=510717337&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 510717337 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00703370 DEM jtl: Demography issn: 00703370 maglogo: N pubinfo: dt: November 2008 vid: 45 iid: 4 pid: 237 pub: Springer Science & Business Media B.V. artinfo: ui: 510717337 10.1353/dem.0.0033 ppf: 785 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P size: 3.8MB tig: atl: Decomposition Method Based on a Model of Continuous Change. aug: au: Horiuchi, Shiro Wilmoth, John R. Pletcher, Scott D. su: Statistics Demography methodology sug: subj: Statistics Demography methodology ab: A demographic measure is often expressed as a deterministic or stochastic function of multiple variables (covariates), and a general problem (the decomposition problem) is to assess contributions of individual covariates to a difference in the demographic measure (dependent variable) between two populations. We propose a method of decomposition analysis based on an assumption that covariates change continuously along an actual or hypothetical dimension. This assumption leads to a general model that logically justifies the additivity of covariate effects and the elimination of interaction terms, even if the dependent variable itself is a nonadditive function. A comparison with earlier methods illustrates other practical advantages of the method: in addition to an absence of residuals or interaction terms, the method can easily handle a large number of covariates and does not require a logically meaningful ordering of covariates. Two empirical examples show that the method can be applied flexibly to a wide variety of decomposition problems. This study also suggests that when data are available at multiple time points over a long interval, it is more accurate to compute an aggregated decomposition based on multiple subintervals than to compute a single decomposition for the entire study period. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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