IDEAS home Printed from https://ideas.repec.org/h/elg/eechap/20207_5.html
   My bibliography  Save this book chapter

Stochastic Projections and Debt

In: Tax Policy and Uncertainty

Author

Listed:
  • .

Abstract

This chapter introduces uncertainty into the deterministic debt projection model outlined in Chapter 3. Stochastic projections are obtained using a non-parametric approach which involves sampling from past joint distributions of those variables subject to uncertainty. The question considered is: what are the implications for the projected path of the probability distribution of the public debt ratio if the future joint variability of a number of component variables is assumed to be similar to that observed in the past? Uncertainly is limited to two major expenditure components, the world interest rate and the rate of productivity growth. It is possible to form probability statements about ranges of the debt ratio in each year, in particular the probability that any given debt ratio is exceeded. The model is used to examine the implications of adopting several income tax policy changes designed to achieve a specified debt ratio by the end of the projection period. Comparisons are made with results using the deterministic version of the model.

Suggested Citation

  • ., 2020. "Stochastic Projections and Debt," Chapters, in: Tax Policy and Uncertainty, chapter 5, pages 113-134, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:20207_5
    as

    Download full text from publisher

    File URL: https://www.elgaronline.com/view/9781800376007.00014.xml
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Gupta, Ankit & Davis, Matthew & Kumar, Amit, 2021. "An integrated assessment framework for the decarbonization of the electricity generation sector," Applied Energy, Elsevier, vol. 288(C).
    2. Dougier, Nathanael & Garambois, Pierre & Gomand, Julien & Roucoules, Lionel, 2021. "Multi-objective non-weighted optimization to explore new efficient design of electrical microgrids," Applied Energy, Elsevier, vol. 304(C).
    3. Liu, Jia & Ma, Tao & Wu, Huijun & Yang, Hongxing, 2023. "Study on optimum energy fuel mix for urban cities integrated with pumped hydro storage and green vehicles," Applied Energy, Elsevier, vol. 331(C).
    4. Bichaye Tesfaye & Monica Lengoiboni & Jaap Zevenbergen & Belay Simane, 2023. "Rethinking the Impact of Land Certification on Tenure Security, Land Disputes, Land Management, and Agricultural Production: Insights from South Wello, Ethiopia," Land, MDPI, vol. 12(9), pages 1-25, September.

    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:elg:eechap:20207_5. 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: Darrel McCalla (email available below). General contact details of provider: http://www.e-elgar.com .

    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.