{"title":"Combining growth and level data: An estimation of the population of Belgian municipalities between 1880 and 1970","authors":"S. Ronsse, Samuel Standaert","doi":"10.1080/01615440.2017.1355764","DOIUrl":null,"url":null,"abstract":"ABSTRACT Economic historians that study long-term changes during the nineteenth and twentieth century are fundamentally restricted by the availability of qualitative data. As a result, researchers are forced to either impute missing data, or otherwise combine datasets in some way. In this article, we demonstrate the versatility of state-space models in addressing these problems. Not only do they enable us to compose large data series of high quality, they also provide a clear estimate of how reliable this data is, allowing any subsequent analyses to take this reliability into account. We illustrate the advantages of a state-space model using the population of Belgian municipalities as a case study. By combining growth and level data, we are able to compute yearly population statistics of over 2600 municipalities from 1880 to 1970.","PeriodicalId":154465,"journal":{"name":"Historical Methods: A Journal of Quantitative and Interdisciplinary History","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Historical Methods: A Journal of Quantitative and Interdisciplinary History","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/01615440.2017.1355764","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
ABSTRACT Economic historians that study long-term changes during the nineteenth and twentieth century are fundamentally restricted by the availability of qualitative data. As a result, researchers are forced to either impute missing data, or otherwise combine datasets in some way. In this article, we demonstrate the versatility of state-space models in addressing these problems. Not only do they enable us to compose large data series of high quality, they also provide a clear estimate of how reliable this data is, allowing any subsequent analyses to take this reliability into account. We illustrate the advantages of a state-space model using the population of Belgian municipalities as a case study. By combining growth and level data, we are able to compute yearly population statistics of over 2600 municipalities from 1880 to 1970.