IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-02128482.html
   My bibliography  Save this paper

Improving the measurement of export instability in the Economic Vulnerability Index: A simple proposal

Author

Listed:
  • Sosso Feindouno

    (FERDI - Fondation pour les Etudes et Recherches sur le Développement International)

Abstract

Alongside GNI per capita and the Human Assets Index (HAI), the Economic Vulnerability Index (EVI) is one of the three criteria used for the identification of the Least Developed Countries (LDCs), as used by the UN-CDP (Committee for Development Policy) at each triennial review of the list of LDCs (see LDC Handbook, United Nations, 2015). EVI has also been proposed as a relevant criterion for the allocation of development assistance (see UN General Assembly resolution on the smooth transition of graduating LDCs – A/C.2/67/L.5 – and a survey in Guillaumont 2009b; Guillaumont and Wagner, 2013). For the EVI to be still considered as a useful tool for the identification of LDCs, and also as an aid allocation criterion, some components (or at least their calculation) may need to be refined.

Suggested Citation

  • Sosso Feindouno, 2019. "Improving the measurement of export instability in the Economic Vulnerability Index: A simple proposal," Post-Print hal-02128482, HAL.
  • Handle: RePEc:hal:journl:hal-02128482
    Note: View the original document on HAL open archive server: https://hal.science/hal-02128482
    as

    Download full text from publisher

    File URL: https://hal.science/hal-02128482/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    2. White Halbert & Granger Clive W.J., 2011. "Consideration of Trends in Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-40, February.
    Full references (including those not matched with items on IDEAS)

    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. Sosso Feindouno, 2019. "Improving the measurement of export instability in the Economic Vulnerability Index: A simple proposal," Post-Print hal-02167897, HAL.
    2. Sosso Feindouno, 2019. "Improving the measurement of export instability in the Economic Vulnerability Index: A simple proposal," Economics Bulletin, AccessEcon, vol. 39(2), pages 1629-1638.
    3. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    4. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    5. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    6. repec:rdg:wpaper:em-dp2013-03 is not listed on IDEAS
    7. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    8. Paulo Júlio & Pedro M. Esperança, 2012. "Evaluating the forecast quality of GDP components: An application to G7," GEE Papers 0047, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Apr 2012.
    9. Cameron Roach & Rob Hyndman & Souhaib Ben Taieb, 2021. "Non‐linear mixed‐effects models for time series forecasting of smart meter demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1118-1130, September.
    10. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    11. Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023. "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, vol. 30(C).
    12. I. Yu. Zolotova & V. V. Dvorkin, 2017. "Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks," Studies on Russian Economic Development, Springer, vol. 28(6), pages 608-615, November.
    13. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    14. Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.
    15. Martin Guth, 2022. "Predicting Default Probabilities for Stress Tests: A Comparison of Models," Papers 2202.03110, arXiv.org.
    16. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    17. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    18. Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
    19. Yamada, Hiroshi & Yoon, Gawon, 2014. "When Grilli and Yang meet Prebisch and Singer: Piecewise linear trends in primary commodity prices," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 193-207.
    20. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    21. Goldsworthy, M. & Moore, T. & Peristy, M. & Grimeland, M., 2022. "Cloud-based model-predictive-control of a battery storage system at a commercial site," Applied Energy, Elsevier, vol. 327(C).
    22. Bialowolski, Piotr & Kuszewski, Tomasz & Witkowski, Bartosz, 2015. "Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 9, pages 1-37.

    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:hal:journl:hal-02128482. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.