A Bayesian Reconstruction of a Historical Population in Finland, 1647–1850.
This article provides a novel method for estimating historical population development. We review the previous literature on historical population time-series estimates and propose a general outline to address the well-known methodological problems. We use a Bayesian hierarchical time-series model th...
| Published in: | Demography (Springer Nature) Vol. 57; no. 3; pp. 1171 - 1193 |
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| Main Authors: | , , |
| Format: | Article |
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Springer Nature
Jun2020
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=144339612&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 144339612 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: Jun2020 vid: 57 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 144339612 10.1007/s13524-020-00889-1 ppf: 1171 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1MB tig: atl: A Bayesian Reconstruction of a Historical Population in Finland, 1647–1850. aug: au: Voutilainen, Miikka Helske, Jouni Högmander, Harri affil: Department of History and Ethnology, University of Jyvaskyla, Jyvaskyla, Finland Department of Mathematics and Statistics, University of Jyvaskyla, Jyvaskyla, Finland Department of Science and Technology, Linköping University, Campus Norrköping, Norrköping, Sweden su: Finland Historical literature sug: subj: Finland Historical literature keyword: Bayesian estimation Early modern era Population growth Population history Bayesian estimation Early modern era Population growth Population history ab: This article provides a novel method for estimating historical population development. We review the previous literature on historical population time-series estimates and propose a general outline to address the well-known methodological problems. We use a Bayesian hierarchical time-series model that allows us to integrate the parish-level data set and prior population information in a coherent manner. The procedure provides us with model-based posterior intervals for the final population estimates. We demonstrate its applicability by estimating the long-term development of Finland's population from 1647 onward and simultaneously place the country among the very few to have an annual population series of such length available. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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