IDEAS home Printed from https://ideas.repec.org/a/ibn/masjnl/v8y2014i5p197.html
   My bibliography  Save this article

About the Method of Analysis of Economic Correlations by Differentiation of Spline Models

Author

Listed:
  • Ruslan Ilyasov

Abstract

The article considers spline approximation as one of efficient methods of modeling economic dynamics. Spline approximation of economic dynamics allows carrying out qualitative and accurate transition from discrete values of a lattice function to a continuous model of a process, which allows calculating values of a studied index at any time point (interpolation). Spline representation improves the quality of economic dynamics modeling while saving the real values of the studied process at each time point. In this article, differentiation of spline models is used for analysis of the economic indexes growth rate. Correlations are detected and itemized by comparison of derivatives. The possibility of detecting "latent trends" is demonstrated by differentiation of spline models of the dynamics using the example of economic indexes of the oil and gas market of Russia. For example, in the first case, we consider spline models of the dynamics of export prices for oil and natural gas. Here, the correlation of the studied indexes is obvious and is detected by both calculation of the correlation ratio and visualization of the studied rows of dynamics with spline models. As an opposite example, we consider the dynamics of the volumes of oil and natural gas export. In this case, we gain the correlation ratio close to zero, which is to evidence absence of correlation. Modeling of the studied dynamics with cubic splines also does not detect any correlation between the dynamics of volumes of the oil and gas export. Our assumptions about "latent trends" are also confirmed by differentiation of spline models – the correlation between the change rate of the volumes of the oil and gas export is detected. Use of spline functions at economic dynamics modeling is determined with such positive properties of theirs as continuity, flexibility, differentiability, the property of minimal curve, etc.

Suggested Citation

  • Ruslan Ilyasov, 2014. "About the Method of Analysis of Economic Correlations by Differentiation of Spline Models," Modern Applied Science, Canadian Center of Science and Education, vol. 8(5), pages 197-197, October.
    • Handle: RePEc:ibn:masjnl:v:8:y:2014:i:5:p:197
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/mas/article/download/40063/22216
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/mas/article/view/40063
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lemmens, A. & Croux, C. & Stremersch, S., 2012. "Dynamics in international market segmentation of new product growth," Other publications TiSEM 306086bd-670f-48d2-97d1-3, Tilburg University, School of Economics and Management.
    2. Aatola, Piia & Ollikainen, Markku & Toppinen, Anne, 2013. "Price determination in the EU ETS market: Theory and econometric analysis with market fundamentals," Energy Economics, Elsevier, vol. 36(C), pages 380-395.
    3. Lemmens, Aurélie & Croux, Christophe & Stremersch, Stefan, 2012. "Dynamics in the international market segmentation of new product growth," International Journal of Research in Marketing, Elsevier, vol. 29(1), pages 81-92.
    4. Benedictow, Andreas & Fjærtoft, Daniel & Løfsnæs, Ole, 2013. "Oil dependency of the Russian economy: An econometric analysis," Economic Modelling, Elsevier, vol. 32(C), pages 400-428.
    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. Jiang, Yuanchun & Liu, Yezheng & Shang, Jennifer & Yildirim, Pinar & Zhang, Qingfu, 2018. "Optimizing online recurring promotions for dual-channel retailers: Segmented markets with multiple objectives," European Journal of Operational Research, Elsevier, vol. 267(2), pages 612-627.
    2. Gelper, Sarah & Stremersch, Stefan, 2014. "Variable selection in international diffusion models," International Journal of Research in Marketing, Elsevier, vol. 31(4), pages 356-367.
    3. Moon, Sangkil & Jalali, Nima & Erevelles, Sunil, 2021. "Segmentation of both reviewers and businesses on social media," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    4. Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
    5. Abramowicz, Konrad & Sjöstedt de Luna, Sara & Strandberg, Johan, 2023. "Nonparametric bagging clustering methods to identify latent structures from a sequence of dependent categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    6. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.
    7. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    8. Feldkircher, Martin, 2015. "A global macro model for emerging Europe," Journal of Comparative Economics, Elsevier, vol. 43(3), pages 706-726.
    9. Yaqi Wu & Chen Zhang & Po Yun & Dandan Zhu & Wei Cao & Zulfiqar Ali Wagan, 2021. "Time–frequency analysis of the interaction mechanism between European carbon and crude oil markets," Energy & Environment, , vol. 32(7), pages 1331-1357, November.
    10. Jouko Rautava, 2013. "Oil Prices, Excess Uncertainty and Trend Growth," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 4, pages 77-87.
    11. Zhu, Bangzhu & Yuan, Lili & Ye, Shunxin, 2019. "Examining the multi-timescales of European carbon market with grey relational analysis and empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 392-399.
    12. repec:ipg:wpaper:2014-552 is not listed on IDEAS
    13. Deryugina, Elena & Ponomarenko, Alexey, 2014. "A large Bayesian vector autoregression model for Russia," BOFIT Discussion Papers 22/2014, Bank of Finland, Institute for Economies in Transition.
    14. Fang Zhang & Zhengjun Zhang, 2020. "The tail dependence of the carbon markets: The implication of portfolio management," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-17, August.
    15. Wen, Xiaoqian & Bouri, Elie & Roubaud, David, 2017. "Can energy commodity futures add to the value of carbon assets?," Economic Modelling, Elsevier, vol. 62(C), pages 194-206.
    16. Lucia Tajoli, 2022. "Too much of a good thing? Russia-EU international trade relations at times of war," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(4), pages 807-834, December.
    17. Nasir, Muhammad Ali & Al-Emadi, Ahmed Abdulsalam & Shahbaz, Muhammad & Hammoudeh, Shawkat, 2019. "Importance of oil shocks and the GCC macroeconomy: A structural VAR analysis," Resources Policy, Elsevier, vol. 61(C), pages 166-179.
    18. Rita Sousa & Luís Aguiar-Conraria, 2014. "Dynamics of CO2 price drivers," NIPE Working Papers 02/2014, NIPE - Universidade do Minho.
    19. Julien Chevallier, 2020. "COVID-19 Outbreak and CO 2 Emissions: Macro-Financial Linkages," JRFM, MDPI, vol. 14(1), pages 1-18, December.
    20. Jiang, Wei & Chen, Yunfei, 2022. "The time-frequency connectedness among carbon, traditional/new energy and material markets of China in pre- and post-COVID-19 outbreak periods," Energy, Elsevier, vol. 246(C).
    21. Fan, John Hua & Todorova, Neda, 2017. "Dynamics of China’s carbon prices in the pilot trading phase," Applied Energy, Elsevier, vol. 208(C), pages 1452-1467.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    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:ibn:masjnl:v:8:y:2014:i:5:p:197. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.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.