IDEAS home Printed from https://ideas.repec.org/p/frd/wpaper/dp2013-04.html
   My bibliography  Save this paper

Maximum Entropy Bootstrap Algorithm Enhancements

Author

Listed:
  • Hrishikesh D. Vinod

    (Fordham University)

Abstract

While moving block bootstrap (MBB) has been used for mildly dependent (m-dependent) time series, maximum entropy (ME) bootstrap (meboot) is perhaps the only tool for inference involving perfectly dependent, nonstationary time series, possibly subject to jumps, regime changes and gaps. This brief note describes the logic and provides the R code for two potential enhancements to the meboot algorithm in Vinod and Lopez-de-Lacalle (2009), available as the 'meboot' package of the R software. The first 'rescaling enhancement' adjusts the of meboot resampled elements so that the population variance of the ME density equals that of the original data. Our second 'symmetrizing enhancement' forces the ME density to be symmetric. One simulation involving inference for regression standard errors suggests that the symmetrizing enhancement of the meboot continues to outperform the MBB.

Suggested Citation

  • Hrishikesh D. Vinod, 2013. "Maximum Entropy Bootstrap Algorithm Enhancements," Fordham Economics Discussion Paper Series dp2013-04, Fordham University, Department of Economics.
    • Handle: RePEc:frd:wpaper:dp2013-04
    as

    Download full text from publisher

    File URL: https://archive.fordham.edu/ECONOMICS_RESEARCH/PAPERS/DP2013_04_Vinod.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vinod, Hrishikesh D. & Lopez-de-Lacalle, Javier, 2009. "Maximum Entropy Bootstrap for Time Series: The meboot R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i05).
    2. Vinod, Hrishikesh D., 2006. "Maximum entropy ensembles for time series inference in economics," Journal of Asian Economics, Elsevier, vol. 17(6), pages 955-978, December.
    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. Honey Karun & Hrishikesh Vinod & Chakraborty, Lekha S., 2020. "Did public investment crowd out private investment in India?," Working Papers 20/312, National Institute of Public Finance and Policy.
    2. Aqil Khan & Mumtaz Ahmed & Salma Bibi, 2019. "Financial development and economic growth nexus for Pakistan: a revisit using maximum entropy bootstrap approach," Empirical Economics, Springer, vol. 57(4), pages 1157-1169, October.
    3. Mariano Méndez-Suárez, 2021. "Marketing Mix Modeling Using PLS-SEM, Bootstrapping the Model Coefficients," Mathematics, MDPI, vol. 9(15), pages 1-12, 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. 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).
    2. Aqil Khan & Mumtaz Ahmed & Salma Bibi, 2019. "Financial development and economic growth nexus for Pakistan: a revisit using maximum entropy bootstrap approach," Empirical Economics, Springer, vol. 57(4), pages 1157-1169, October.
    3. Pedro Macedo & Mara Madaleno, 2022. "Global Temperature and Carbon Dioxide Nexus: Evidence from a Maximum Entropy Approach," Energies, MDPI, vol. 16(1), pages 1-13, December.
    4. Arisara Romyen & Chukiat Chaiboonsri & Satawat Wannapan & Songsak Sriboonchitta, 2019. "Multi-Process-Based Maximum Entropy Bootstrapping Estimator: Application for Net Foreign Direct Investment in ASEAN," Economies, MDPI, vol. 7(3), pages 1-13, July.
    5. Yalta, A. Yasemin, 2013. "Revisiting the FDI-led growth Hypothesis: The case of China," Economic Modelling, Elsevier, vol. 31(C), pages 335-343.
    6. A. Talha Yalta, 2016. "Bootstrap Inference of Level Relationships in the Presence of Serially Correlated Errors: A Large Scale Simulation Study and an Application in Energy Demand," Computational Economics, Springer;Society for Computational Economics, vol. 48(2), pages 339-366, August.
    7. A. Talha Yalta, 2013. "The Dynamics of Road Energy Demand and Illegal Fuel Activity in Turkey: A Rolling Window Analysis," Working Papers 1304, TOBB University of Economics and Technology, Department of Economics, revised Jul 2013.
    8. Yalta, A. Talha & Yalta, A. Yasemin, 2016. "The dynamics of fuel demand and illegal fuel activity in Turkey," Energy Economics, Elsevier, vol. 54(C), pages 144-158.
    9. Miroslav Plašil, 2011. "Potenciální produkt, mezera výstupu a míra nejistoty spojená s jejich určením při použití Hodrick-Prescottova filtru [Potential Product, Output Gap and Uncertainty Rate Associated with Their Determ," Politická ekonomie, Prague University of Economics and Business, vol. 2011(4), pages 490-507.
    10. Vacca, Gianmarco & Zoia, Maria Grazia & Bagnato, Luca, 2022. "Forecasting in GARCH models with polynomially modified innovations," International Journal of Forecasting, Elsevier, vol. 38(1), pages 117-141.
    11. Alptekin, Aynur & Broadstock, David C. & Chen, Xiaoqi & Wang, Dong, 2019. "Time-varying parameter energy demand functions: Benchmarking state-space methods against rolling-regressions," Energy Economics, Elsevier, vol. 82(C), pages 26-41.
    12. A. Talha Yalta, 2013. "Small Sample Bootstrap Inference of Level Relationships in the Presence of Autocorrelated Errors: A Large Scale Simulation Study and an Application in Energy Demand," Working Papers 1301, TOBB University of Economics and Technology, Department of Economics.
    13. Zeinab Zanjani & Pedro Macedo & Isabel Soares, 2021. "Investigating Carbon Emissions from Electricity Generation and GDP Nexus Using Maximum Entropy Bootstrap: Evidence from Oil-Producing Countries in the Middle East," Energies, MDPI, vol. 14(12), pages 1-22, June.
    14. Galip Altinay & A. Talha Yalta, 2016. "Estimating the evolution of elasticities of natural gas demand: the case of Istanbul, Turkey," Empirical Economics, Springer, vol. 51(1), pages 201-220, August.
    15. H.D. Vinod, 2016. "New bootstrap inference for spurious regression problems," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(2), pages 317-335, February.
    16. Maria Grazia Zoia & Gianmarco Vacca & Laura Barbieri, 2020. "Modeling Multivariate Financial Series and Computing Risk Measures via Gram–Charlier-Like Expansions," Risks, MDPI, vol. 8(4), pages 1-21, November.
    17. Hrishikesh Vinod & Lekha S. Chakraborty & Honey Karun, 2014. "If Deficits Are Not the Culprit, What Determines Indian Interest Rates? An Evaluation Using the Maximum Entropy Bootstrap Method," Economics Working Paper Archive wp_811, Levy Economics Institute.
    18. A. Yasemin Yalta, 2011. "New Evidence on FDI-Led Growth: The Case of China," Working Papers 1107, TOBB University of Economics and Technology, Department of Economics.
    19. Zanjani, Zeinab & Soares, Isabel & Macedo, Pedro, 2023. "Investigating CO2 emissions from aviation in oil producing countries using a two-stage maximum entropy approach," Energy, Elsevier, vol. 278(PA).
    20. E. Ramos-P'erez & P. J. Alonso-Gonz'alez & J. J. N'u~nez-Vel'azquez, 2020. "Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network," Papers 2006.16383, arXiv.org, revised Aug 2020.

    More about this item

    Keywords

    Maximum entropy; block bootstrap; variance; symmetry; R-software;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:frd:wpaper:dp2013-04. 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: Fordham Economics (email available below). General contact details of provider: https://edirc.repec.org/data/edforus.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.