IDEAS home Printed from https://ideas.repec.org/a/taf/vhimxx/v50y2017i4p218-226.html
   My bibliography  Save this article

Combining growth and level data: An estimation of the population of Belgian municipalities between 1880 and 1970

Author

Listed:
  • Stijn Ronsse
  • Samuel Standaert

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.

Suggested Citation

  • Stijn Ronsse & Samuel Standaert, 2017. "Combining growth and level data: An estimation of the population of Belgian municipalities between 1880 and 1970," Historical Methods: A Journal of Quantitative and Interdisciplinary History, Taylor & Francis Journals, vol. 50(4), pages 218-226, October.
    • Handle: RePEc:taf:vhimxx:v:50:y:2017:i:4:p:218-226
    DOI: 10.1080/01615440.2017.1355764
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01615440.2017.1355764
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01615440.2017.1355764?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    2. González-Val, Rafael & Lanaspa, Luis & Sanz, Fernando, 2008. "New Evidence on Gibrat’s Law for Cities," MPRA Paper 10411, University Library of Munich, Germany.
    3. Desbordes, Rodolphe & Koop, Gary, 2016. "Should we care about the uncertainty around measures of political-economic development?," Journal of Comparative Economics, Elsevier, vol. 44(3), pages 752-763.
    4. Veenstra, Joost, 2015. "Output growth in German manufacturing, 1907–1936. A reinterpretation of time-series evidence," Explorations in Economic History, Elsevier, vol. 57(C), pages 38-49.
    5. Samuel Standaert & Stijn Ronsse & Benjamin Vandermarliere, 2016. "Historical trade integration: globalization and the distance puzzle in the long twentieth century," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 10(2), pages 225-250, may.
    6. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ronan Lyons & Elisa Maria Tirindelli, 2022. "The Rise & Fall of Urban Concentration in Britain: Zipf, Gibrat and Gini across two centuries," Trinity Economics Papers tep0522, Trinity College Dublin, Department of Economics.
    2. Rafael González-Val & Javier Silvestre, 2020. "An annual estimate of spatially disaggregated populations: Spain, 1900–2011," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 65(2), pages 491-508, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stijn Ronsse & Samuel Standaert, 2017. "Combining growth and level data: an estimation of the population of Belgian cities between 1880 and 1970," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 17/927, Ghent University, Faculty of Economics and Business Administration.
    2. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model," Tinbergen Institute Discussion Papers 08-069/4, Tinbergen Institute.
    3. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.
    4. Davide Delle Monache & Ivan Petrella, 2014. "Adaptive Models and Heavy Tails," Working Papers 720, Queen Mary University of London, School of Economics and Finance.
    5. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2017. "Does the ARFIMA really shift?," CREATES Research Papers 2017-16, Department of Economics and Business Economics, Aarhus University.
    6. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    7. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    8. Berger, Tino & Pozzi, Lorenzo, 2013. "Measuring time-varying financial market integration: An unobserved components approach," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 463-473.
    9. Samuel N. Cohen & Silvia Lui & Will Malpass & Giulia Mantoan & Lars Nesheim & 'Aureo de Paula & Andrew Reeves & Craig Scott & Emma Small & Lingyi Yang, 2023. "Nowcasting with signature methods," Papers 2305.10256, arXiv.org.
    10. Ai Deng & Pierre Perron, 2006. "A comparison of alternative asymptotic frameworks to analyse a structural change in a linear time trend," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 423-447, November.
    11. Francis Vitek, 2005. "An Unobserved Components Model of the Monetary Transmission Mechanism in a Small Open Economy," Macroeconomics 0512019, University Library of Munich, Germany, revised 06 Feb 2006.
    12. Nima Nonejad, 2021. "Bayesian model averaging and the conditional volatility process: an application to predicting aggregate equity returns by conditioning on economic variables," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1387-1411, August.
    13. Malin Gardberg & Lorenzo Pozzi, 2022. "Aggregate consumption and wealth in the long run: The impact of financial liberalization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 161-186, January.
    14. Justin Yifu Lin & Célestin Monga & Samuel Standaert, 2019. "The Inclusive Sustainable Transformation Index," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(1), pages 47-80, May.
    15. repec:spo:wpmain:info:hdl:2441/2129 is not listed on IDEAS
    16. Nima Nonejad, 2013. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," CREATES Research Papers 2013-27, Department of Economics and Business Economics, Aarhus University.
    17. Jana Riedel, 2020. "On real interest rate convergence among G7 countries," Empirical Economics, Springer, vol. 59(2), pages 599-626, August.
    18. Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2017. "Tracking the Slowdown in Long-Run GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 343-356, May.
    19. Berger, Tino & Richter, Julia, 2017. "What has caused global business cycle decoupling: Smaller shocks or reduced sensitivity?," University of Göttingen Working Papers in Economics 300, University of Goettingen, Department of Economics.
    20. Rokas Gylys & Jonas Šiaulys, 2020. "Estimation of Uncertainty in Mortality Projections Using State-Space Lee-Carter Model," Mathematics, MDPI, vol. 8(7), pages 1-23, June.
    21. Matthieu Lemoine & Florian Pelgrin, 2003. "Introduction aux modèles espace-état et au filtre de Kalman," Revue de l'OFCE, Presses de Sciences-Po, vol. 86(3), pages 203-229.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:vhimxx:v:50:y:2017:i:4:p:218-226. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/vhim20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.