Probabilistic forecasting using stochastic diffusion models, with applications to cohort processes of marriage and fertility.
In this article, we show how stochastic diffusion models can be used to forecast demographic cohort processes using the Hernes, Gompertz, and logistic models. Such models have been used deterministically in the past, but both behavioral theory and forecast utility are improved by introducing randomn...
| Publicado en: | Demography (Springer Nature) Vol. 50; no. 1; pp. 237 - 261 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=108022129&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 108022129 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00703370 DEM jtl: Demography (Springer Nature) issn: 00703370 maglogo: N pubinfo: dt: Feb2013 vid: 50 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 108022129 NLM23104205 2011910661 10.1007/s13524-012-0154-4 NLM23104205 108022129 ppf: 237 ppct: 24 formats: fmt: @attributes: type: P tig: atl: Probabilistic forecasting using stochastic diffusion models, with applications to cohort processes of marriage and fertility. aug: au: Myrskylä M Goldstein JR Myrskylä, Mikko Goldstein, Joshua R affil: Research Group Lifecourse Dynamics and Demographic Change, Max Planck Institute for Demographic Research, Rostock, Germany sug: subj: Birth Rate Marital Status Systems Analysis Adult Age Factors Europe Middle Age Statistics Time Factors Uncertainty Adult: 19-44 years Middle Aged: 45-64 years ab: In this article, we show how stochastic diffusion models can be used to forecast demographic cohort processes using the Hernes, Gompertz, and logistic models. Such models have been used deterministically in the past, but both behavioral theory and forecast utility are improved by introducing randomness and uncertainty into the standard differential equations governing population processes. Our approach is to add time-series stochasticity to linearized versions of each process. We derive both Monte Carlo and analytic methods for estimating forecast uncertainty. We apply our methods to several examples of marriage and fertility, extending them to simultaneous forecasting of multiple cohorts and to processes restricted by factors such as declining fecundity. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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