IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v16y2023i10p431-d1251709.html
   My bibliography  Save this article

Network Activity and Ethereum Gas Prices

Author

Listed:
  • Dimitrios Koutmos

    (Department of Accounting, Finance, and Business Law, College of Business, Texas A&M University—Corpus Christi, Corpus Christi, TX 78412, USA)

Abstract

This article explores the extent to which network activity can explain changes in Ethereum transaction fees. Such fees are referred to as “gas prices” within the Ethereum blockchain, and are important inputs not only for executing transactions, but also for the deployment of smart contracts within the network. Using a bootstrapped quantile regression model, it can be shown that network activity, such as the sizes of blocks or the number of transactions and contracts, can have a heterogeneous relationship with gas prices across periods of low and high gas price changes. Of all the network activity variables examined herein, the number of intraday transactions within Ethereum’s blockchain is most consistent in explaining gas fees across the full distribution of gas fee changes. From a statistical perspective, the bootstrapped quantile regression approach demonstrates that linear modeling techniques may yield but a partial view of the rich dynamics found in the full range of gas price changes’ conditional distribution. This is an important finding given that Ethereum’s blockchain has undergone fundamental economic and technological regime changes, such as the recent implementation of the Ethereum Improvement Proposal (EIP) 1559, which aims to provide an algorithmic updating rule to estimate Ethereum’s “base fee”.

Suggested Citation

  • Dimitrios Koutmos, 2023. "Network Activity and Ethereum Gas Prices," JRFM, MDPI, vol. 16(10), pages 1-14, September.
  • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:10:p:431-:d:1251709
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/16/10/431/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/16/10/431/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cynthia Weiyi Cai, 2018. "Disruption of financial intermediation by FinTech: a review on crowdfunding and blockchain," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(4), pages 965-992, December.
    2. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-458, March.
    3. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    4. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
    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. Alexander Karaivanov and Shayan Zarifian, 2024. "Economic Determinants of Ethereum Transaction Fees in the Priority Fee and Proof of Stake Periods," Discussion Papers dp24-02, Department of Economics, Simon Fraser University.

    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. Umer Shahzad & Magdalena Radulescu & Syed Rahim & Cem Isik & Zahid Yousaf & Stefan Alexandru Ionescu, 2021. "Do Environment-Related Policy Instruments and Technologies Facilitate Renewable Energy Generation? Exploring the Contextual Evidence from Developed Economies," Energies, MDPI, vol. 14(3), pages 1-25, January.
    2. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    3. Ma, Qiang & Mentel, Grzegorz & Zhao, Xin & Salahodjaev, Raufhon & Kuldasheva, Zebo, 2022. "Natural resources tax volatility and economic performance: Evaluating the role of digital economy," Resources Policy, Elsevier, vol. 75(C).
    4. Paolo Naticchioni & Andrea Ricci & Emiliano Rustichelli, 2007. "Wage Structure, Inequality And Skill-Biased Change: Is Italy An Outlier?," Quaderni del Dipartimento di Economia, Finanza e Statistica 38/2007, Università di Perugia, Dipartimento Economia.
    5. Moshe Buchinsky & Jinyong Hahn, 1998. "An Alternative Estimator for the Censored Quantile Regression Model," Econometrica, Econometric Society, vol. 66(3), pages 653-672, May.
    6. Anu, & Singh, Amit Kumar & Raza, Syed Ali & Nakonieczny, Joanna & Shahzad, Umer, 2023. "Role of financial inclusion, green innovation, and energy efficiency for environmental performance? Evidence from developed and emerging economies in the lens of sustainable development," Structural Change and Economic Dynamics, Elsevier, vol. 64(C), pages 213-224.
    7. Omar Arias & Walter Sosa-Escudero & Kevin F. Hallock, 2001. "Individual heterogeneity in the returns to schooling: instrumental variables quantile regression using twins data," Empirical Economics, Springer, vol. 26(1), pages 7-40.
    8. Pamela Giustinelli, 2004. "Quantile Regression Evidence on Italian Education Returns," Rivista di Politica Economica, SIPI Spa, vol. 94(6), pages 49-100, November-.
    9. Wu, Di & Yang, Yuping & Shi, Yi & Xu, Meng & Zou, Wenjie, 2022. "Renewable energy resources, natural resources volatility and economic performance: Evidence from BRICS," Resources Policy, Elsevier, vol. 76(C).
    10. James Alm & Sally Wallace, 2007. "Which Elasticity? Estimating the Responsiveness of Taxpayer Reporting Decisions," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 13(3), pages 255-267, August.
    11. Yu-Yen Ku & Tze-Yu Yen, 2016. "Heterogeneous Effect of Financial Leverage on Corporate Performance: A Quantile Regression Analysis of Taiwanese Companies," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 1-33, September.
    12. Ming-Yuan Leon Li, 2009. "Reexamining asymmetric effects of monetary and government spending policies on economic growth using quantile regression," Journal of Developing Areas, Tennessee State University, College of Business, vol. 43(1), pages 137-154, September.
    13. Buchinsky, Moshe, 1995. "Quantile regression, Box-Cox transformation model, and the U.S. wage structure, 1963-1987," Journal of Econometrics, Elsevier, vol. 65(1), pages 109-154, January.
    14. Chen, Songnian, 2018. "Sequential estimation of censored quantile regression models," Journal of Econometrics, Elsevier, vol. 207(1), pages 30-52.
    15. Akosah, Nana Kwame & Alagidede, Imhotep Paul & Schaling, Eric, 2020. "Testing for asymmetry in monetary policy rule for small-open developing economies: Multiscale Bayesian quantile evidence from Ghana," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    16. Rossana, Robert J., 1988. "Interrelated Demands for Buffer Stocks and Productive Inputs: Estimates for Two-Digit Manufacturing Industries," Department of Economics and Business - Archive 259428, North Carolina State University, Department of Economics.
    17. Michel DIMOU & Alexandra SCHAFFAR & Zhihong CHEN & Shihe FU, 2008. "LA CROISSANCE URBAINE CHINOISE RECONSIDeReE," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 27, pages 109-131.
    18. Bosker, Maarten & Brakman, Steven & Garretsen, Harry & Schramm, Marc, 2008. "A century of shocks: The evolution of the German city size distribution 1925-1999," Regional Science and Urban Economics, Elsevier, vol. 38(4), pages 330-347, July.
    19. Bierens, H.J. & Broersma, L., 1991. "The relation between unemployment and interest rate : some international evidence," Serie Research Memoranda 0112, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    20. Muhammad Zia Ullah Khan & Muhammad Illyas & Muqqadas Rahman & Chaudhary Abdul Rahman, 2015. "Money Monetization and Economic Growth in Pakistan," International Journal of Economics and Empirical Research (IJEER), The Economics and Social Development Organization (TESDO), vol. 3(4), pages 184-192, April.

    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:jjrfmx:v:16:y:2023:i:10:p:431-:d:1251709. 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.