IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v4y2016i1p16-d65623.html
   My bibliography  Save this article

New Method of Randomized Forecasting Using Entropy-Robust Estimation: Application to the World Population Prediction

Author

Listed:
  • Yuri S. Popkov

    (Institute for Systems Analysis of Russian Academy of Sciences, Moscow 117312, Russia
    Moscow Institute of Physics and Technology (State University), Moscow 141700, Russia
    National Research University Higher School of Economics, Moscow 101000, Russia)

  • Yuri A. Dubnov

    (Institute for Systems Analysis of Russian Academy of Sciences, Moscow 117312, Russia
    Moscow Institute of Physics and Technology (State University), Moscow 141700, Russia)

  • Alexey Yu. Popkov

    (Institute for Systems Analysis of Russian Academy of Sciences, Moscow 117312, Russia
    Moscow Institute of Physics and Technology (State University), Moscow 141700, Russia)

Abstract

We propose a new method of randomized forecasting (RF-method), which operates with models described by systems of linear ordinary differential equations with random parameters. The RF-method is based on entropy-robust estimation of the probability density functions (PDFs) of model parameters and measurement noises. The entropy-optimal estimator uses a limited amount of data. The method of randomized forecasting is applied to World population prediction. Ensembles of entropy-optimal prognostic trajectories of World population and their probability characteristics are generated. We show potential preferences of the proposed method in comparison with existing methods.

Suggested Citation

  • Yuri S. Popkov & Yuri A. Dubnov & Alexey Yu. Popkov, 2016. "New Method of Randomized Forecasting Using Entropy-Robust Estimation: Application to the World Population Prediction," Mathematics, MDPI, vol. 4(1), pages 1-16, March.
    • Handle: RePEc:gam:jmathe:v:4:y:2016:i:1:p:16-:d:65623
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/4/1/16/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/4/1/16/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Horvath, Roman, 2011. "Research & development and growth: A Bayesian model averaging analysis," Economic Modelling, Elsevier, vol. 28(6), pages 2669-2673.
    2. Myles R. Allen & David A. Stainforth, 2002. "Towards objective probabalistic climate forecasting," Nature, Nature, vol. 419(6903), pages 228-228, September.
    3. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    4. Roman Horváth, 2011. "Research & Development and Long-Term Economic Growth: A Bayesian Model Averaging Analysis," Working Papers IES 2011/19, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jun 2011.
    5. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
    6. Kocięcki, Andrzej & Kolasa, Marcin & Rubaszek, Michał, 2012. "A Bayesian method of combining judgmental and model-based density forecasts," Economic Modelling, Elsevier, vol. 29(4), pages 1349-1355.
    7. Popkov, Alexey Y. & Popkov, Yuri S. & van Wissen, Leo, 2005. "Positive dynamic systems with entropy operator: Application to labour market modelling," European Journal of Operational Research, Elsevier, vol. 164(3), pages 811-828, August.
    8. Kim, Michael Jong & Jiang, Rui & Makis, Viliam & Lee, Chi-Guhn, 2011. "Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure," European Journal of Operational Research, Elsevier, vol. 214(2), pages 331-339, October.
    9. Cho, Sungbin, 2009. "A linear Bayesian stochastic approximation to update project duration estimates," European Journal of Operational Research, Elsevier, vol. 196(2), pages 585-593, July.
    10. Musal, R. Muzaffer & Soyer, Refik & McCabe, Christopher & Kharroubi, Samer A., 2012. "Estimating the population utility function: A parametric Bayesian approach," European Journal of Operational Research, Elsevier, vol. 218(2), pages 538-547.
    11. Zellner, Arnold, 2010. "Bayesian shrinkage estimates and forecasts of individual and total or aggregate outcomes," Economic Modelling, Elsevier, vol. 27(6), pages 1392-1397, November.
    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. Yuri S. Popkov & Yuri A. Dubnov & Alexey Yu. Popkov, 2023. "Reinforcement Procedure for Randomized Machine Learning," Mathematics, MDPI, vol. 11(17), pages 1-14, August.

    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. Balázs Égert, 2016. "Regulation, Institutions, and Productivity: New Macroeconomic Evidence from OECD Countries," American Economic Review, American Economic Association, vol. 106(5), pages 109-113, May.
    2. Zafar, Muhammad Wasif & Shahbaz, Muhammad & Hou, Fujun & Sinha, Avik, 2018. "¬¬¬¬¬¬From Nonrenewable to Renewable Energy and Its Impact on Economic Growth: Silver Line of Research & Development Expenditures in APEC Countries," MPRA Paper 90611, University Library of Munich, Germany, revised 10 Dec 2018.
    3. Mehmet Balcilar & Ojonugwa Usman & George N. Ike, 2023. "Investing green for sustainable development without ditching economic growth," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(2), pages 728-743, April.
    4. Niu, Linlin & Xu, Xiu & Chen, Ying, 2017. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," Economic Modelling, Elsevier, vol. 66(C), pages 201-213.
    5. Marcin Błażejowski & Jacek Kwiatkowski & Jakub Gazda, 2019. "Sources of Economic Growth: A Global Perspective," Sustainability, MDPI, vol. 11(1), pages 1-14, January.
    6. John Inekwe, 2015. "The Contribution of R&D Expenditure to Economic Growth in Developing Economies," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 124(3), pages 727-745, December.
    7. Ulger, Ozlem & Uçan, Okyay, 2018. "R&D expenditures - growth nexus in Turkey," MPRA Paper 91060, University Library of Munich, Germany.
    8. Abida Hafeez & Karim Bux Shah Syed & Fiza Qureshi, 2019. "Exploring the Relationship between Government R & D Expenditures and Economic Growth in a Global Perspective: A PMG Estimation Approach," International Business Research, Canadian Center of Science and Education, vol. 12(4), pages 163-174, April.
    9. Chandrashekar Raghutla & Krishna Reddy Chittedi, 2021. "Financial development, real sector and economic growth: Evidence from emerging market economies," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 6156-6167, October.
    10. Begüm Erdil Şahin, 2015. "The Relationship Between R&D Expenditures and Economic Growth: Panel Data Analysis 1990-2013," EY International Congress on Economics II (EYC2015), November 5-6, 2015, Ankara, Turkey 207, Ekonomik Yaklasim Association.
    11. Balázs Egert, 2017. "The Impact Of Regulations And Institutions On Multi-Factor Productivity: New Evidence From Macroeconomic Estimates," Working Papers hal-04141653, HAL.
    12. Lucchetti, Riccardo & Pedini, Luca & Pigini, Claudia, 2022. "No such thing as the perfect match: Bayesian Model Averaging for treatment evaluation," Economic Modelling, Elsevier, vol. 107(C).
    13. Acheampong, Alex O. & Dzator, Janet & Dzator, Michael & Salim, Ruhul, 2022. "Unveiling the effect of transport infrastructure and technological innovation on economic growth, energy consumption and CO2 emissions," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    14. Baecke, Philippe & De Baets, Shari & Vanderheyden, Karlien, 2017. "Investigating the added value of integrating human judgement into statistical demand forecasting systems," International Journal of Production Economics, Elsevier, vol. 191(C), pages 85-96.
    15. Schultze, Thomas & Schulz-Hardt, Stefan, 2015. "The impact of biased information and corresponding meta-information on escalating commitment," Journal of Economic Psychology, Elsevier, vol. 49(C), pages 108-119.
    16. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    17. Patrick Krieger & Carsten Lausberg, 2021. "Entscheidungen, Entscheidungsfindung und Entscheidungsunterstützung in der Immobilienwirtschaft: Eine systematische Literaturübersicht [Decisions, decision-making and decisions support systems in r," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 7(1), pages 1-33, April.
    18. Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos, 2022. "Relative performance of judgmental methods for forecasting the success of megaprojects," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1185-1196.
    19. Rybacki, Jakub & Gniazdowski, Michał, 2021. "Macroeconomic Forecasting in Poland: Lessons From the COVID-19 Outbreak," MPRA Paper 107682, University Library of Munich, Germany.
    20. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.

    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:gam:jmathe:v:4:y:2016:i:1:p:16-:d:65623. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.