IDEAS home Printed from https://ideas.repec.org/p/ese/cempwp/cempa9-25.html
   My bibliography  Save this paper

Machine learning regionalisation of input data for microsimulation models: An application of a hybrid GBM / IPF method to build a tax-benefit model for the Essex region in the UK

Author

Listed:
  • Richiardi, Matteo
  • Rejoice, Frimpong

Abstract

Development of microsimulation models often requires reweighting some input dataset to reflect the characteristics of a different population of interest. In this paper we explore a machine learning approach whereas a variant of decision trees (Gradient Boosted Machine) is used to replicate the joint distribution of target variables observed in a large commercially available but slightly biased dataset, with an additional raking step to remove the bias and ensure consistency of relevant marginal distributions with official statistics. The method is applied to build a regional variant of UKMOD, an open-source static tax-benefit model for the UK belonging to the EUROMOD family, with an application to the Greater Essex region in the UK.

Suggested Citation

  • Richiardi, Matteo & Rejoice, Frimpong, 2025. "Machine learning regionalisation of input data for microsimulation models: An application of a hybrid GBM / IPF method to build a tax-benefit model for the Essex region in the UK," Centre for Microsimulation and Policy Analysis Working Paper Series CEMPA9/25, Centre for Microsimulation and Policy Analysis at the Institute for Social and Economic Research.
    • Handle: RePEc:ese:cempwp:cempa9-25
    as

    Download full text from publisher

    File URL: https://www.iser.essex.ac.uk/wp-content/uploads/files/working-papers/cempa/cempa9-25.pdf
    Download Restriction: no
    ---><---

    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:ese:cempwp:cempa9-25. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Jonathan Nears (email available below). General contact details of provider: https://edirc.repec.org/data/rcessuk.html .

    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.