IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i7p1159-d786399.html

Coherence Coefficient for Official Statistics

Author

Listed:
  • Danutė Krapavickaitė

    (Department of Mathematical Statistics, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania)

Abstract

One of the quality requirements in official statistics is coherence of statistical information across domains, in time, in national accounts, and internally. However, no measure of its strength is used. The concept of coherence is also met in signal processing, wave physics, and time series. In the current article, the definition of the coherence coefficient for a weakly stationary time series is recalled and discussed. The coherence coefficient is a correlation coefficient between two indicators in time indexed by the same frequency components of their Fourier transforms and shows a degree of synchronicity between the time series for each frequency. The usage of this coefficient is illustrated through the coherence and Granger causality analysis of a collection of numerical economic and social statistical indicators. The coherence coefficient matrix-based non-metric multidimensional scaling for visualization of the time series in the frequency domain is a newly suggested method. The aim of this article is to propose the use of this coherence coefficient and its applications in official statistics.

Suggested Citation

  • Danutė Krapavickaitė, 2022. "Coherence Coefficient for Official Statistics," Mathematics, MDPI, vol. 10(7), pages 1-20, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1159-:d:786399
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/7/1159/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/7/1159/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gintautas Dzemyda & Olga Kurasova & Julius Žilinskas, 2013. "Multidimensional Data Visualization," Springer Optimization and Its Applications, Springer, edition 127, number 978-1-4419-0236-8, March.
    2. Miguel F. M. Lima & J. A. Tenreiro Machado & António C. Costa, 2012. "A Multidimensional Scaling Analysis of Musical Sounds Based on Pseudo Phase Plane," Abstract and Applied Analysis, Hindawi, vol. 2012, pages 1-14, July.
    3. Donatello Materassi & Giacomo Innocenti, 2008. "Coherence-based multivariate analysis of high frequency stock market values," Papers 0805.2713, arXiv.org.
    4. Gintautas Dzemyda & Olga Kurasova & Julius Žilinskas, 2013. "Applications of Visualization," Springer Optimization and Its Applications, in: Multidimensional Data Visualization, edition 127, chapter 0, pages 179-226, Springer.
    5. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    6. G. Innocenti & D. Materassi, 2008. "Econometrics as Sorcery," Papers 0801.3047, arXiv.org.
    7. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, vol. 132(2), pages 363-378, June.
    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. Ciner, Cetin, 2011. "Eurocurrency interest rate linkages: A frequency domain analysis," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 498-505, October.
    2. Barunik, Jozef & Krehlik, Tomas, 2016. "Measuring the frequency dynamics of financial and macroeconomic connectedness," FinMaP-Working Papers 54, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    3. Bouoiyour, Jamal & Selmi, Refk & Tiwari, Aviral Kumar & Shahbaz, Muhammad, 2015. "The nexus between oil price and Russia's real exchange rate: Better paths via unconditional vs conditional analysis," Energy Economics, Elsevier, vol. 51(C), pages 54-66.
    4. Tiwari, Aviral Kumar, 2012. "An empirical investigation of causality between producers' price and consumers' price indices in Australia in frequency domain," Economic Modelling, Elsevier, vol. 29(5), pages 1571-1578.
    5. Breitung, Jörg & Schreiber, Sven, 2018. "Assessing causality and delay within a frequency band," Econometrics and Statistics, Elsevier, vol. 6(C), pages 57-73.
    6. repec:ipg:wpaper:2014-108 is not listed on IDEAS
    7. Wang, Lu & Ma, Feng & Niu, Tianjiao & Liang, Chao, 2021. "The importance of extreme shock: Examining the effect of investor sentiment on the crude oil futures market," Energy Economics, Elsevier, vol. 99(C).
    8. Alqahtani, Faisal & Hamdi, Besma & Hammoudeh, Shawkat, 2021. "The effects of global factors on the Saudi Arabia equity market by firm size: Implications for risk management based on quantile analysis and frequency domain causality," Journal of Multinational Financial Management, Elsevier, vol. 61(C).
    9. Jozef Baruník & Tomáš Křehlík, 2018. "Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 271-296.
    10. Ciner, Cetin, 2011. "Commodity prices and inflation: Testing in the frequency domain," Research in International Business and Finance, Elsevier, vol. 25(3), pages 229-237, September.
    11. Tsangyao Chang & Omid Ranjbar & Charl Jooste, 2017. "Stock Market Interactions between the BRICS and the United States: Evidence from Asymmetric Granger Causality Tests in the Frequency Domain," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 21(2), pages 297-320, Spring.
    12. Bouoiyour, Jamal & Gauthier, Marie & Bouri, Elie, 2023. "Which is leading: Renewable or brown energy assets?," Energy Economics, Elsevier, vol. 117(C).
    13. Roman Hušek & Tomáš Formánek, 2014. "Alternative specification, estimation and identification of vector autoregressions [Alternativní specifikace, odhad a identifikace vektorových autoregresí]," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2014(4), pages 52-72.
    14. Saffet Akdag & Ömer İskenderoglu & Andrew Adewale Alola, 2020. "The volatility spillover effects among risk appetite indexes: insight from the VIX and the rise," Letters in Spatial and Resource Sciences, Springer, vol. 13(1), pages 49-65, April.
    15. Joseph, Anto & Sisodia, Garima & Tiwari, Aviral Kumar, 2014. "A frequency domain causality investigation between futures and spot prices of Indian commodity markets," Economic Modelling, Elsevier, vol. 40(C), pages 250-258.
    16. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    17. Bathia, Deven & Demirer, Riza & Gupta, Rangan & Kotzé, Kevin, 2021. "Unemployment fluctuations and currency returns in the United Kingdom: Evidence from over one and a half century of data," Journal of Multinational Financial Management, Elsevier, vol. 61(C).
    18. Arturas Kaklauskas & Edmundas Kazimieras Zavadskas & Bjoern Schuller & Natalija Lepkova & Gintautas Dzemyda & Jurate Sliogeriene & Olga Kurasova, 2020. "Customized ViNeRS Method for Video Neuro-Advertising of Green Housing," IJERPH, MDPI, vol. 17(7), pages 1-28, March.
    19. Jamal Bouoiyour & Refk Selmi & Amal Miftah, 2019. "The relationship between remittances and macroeconomic variables in times of political and social upheaval: Evidence from Tunisia's Arab Spring," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 27(2), pages 355-394, February.
    20. Richard A. Ashley & Kwok Ping Tsang, 2014. "Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach," Econometrics, MDPI, vol. 2(1), pages 1-20, March.
    21. Theologos Dergiades & Panos K. Pouliasis, 2023. "Should stock returns predictability be ‘hooked on’ long‐horizon regressions?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 718-732, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:gam:jmathe:v:10:y:2022:i:7:p:1159-:d:786399. 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 The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (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.