IDEAS home Printed from https://ideas.repec.org/a/bla/ausecr/v53y2020i2p270-291.html
   My bibliography  Save this article

Visualising Time: A Compendium of Time Series Plots

Author

Abstract

Graphs are important for highlighting relationships within a data series or across several series. Modern computer software has provided flexibility in the construction of graphic displays that would have been impossible with the tools that were available to researchers only a few decades ago. This article illustrates a variety of different graphical presentations for time ordered or time series data that can now be constructed. These include time series plots, bar charts, range plots, radar charts, scatter plots, heat maps and seasonality plots. For each graph type presented, we discuss the best practice for their construction.

Suggested Citation

  • Joe Hirschberg & Jenny Lye, 2020. "Visualising Time: A Compendium of Time Series Plots," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 53(2), pages 270-291, June.
    • Handle: RePEc:bla:ausecr:v:53:y:2020:i:2:p:270-291
    DOI: 10.1111/1467-8462.12374
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-8462.12374
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-8462.12374?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Stock, James H. & Watson, Mark W., 2014. "Estimating turning points using large data sets," Journal of Econometrics, Elsevier, vol. 178(P2), pages 368-381.
    2. Michael N. Mitchell, 2012. "A Visual Guide to Stata Graphics, 3rd Edition," Stata Press books, StataCorp LP, number vgsg.
    3. Watson, Mark W. & Stock, James H., 2014. "Estimating turning points using large data sets," Scholarly Articles 33192198, Harvard University Department of Economics.
    4. Ibrahim Demir & Robert D. Tollison, 2015. "Graphs in Economics," Economics Bulletin, AccessEcon, vol. 35(3), pages 1834-1847.
    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. Aastveit, Knut Are & Jore, Anne Sofie & Ravazzolo, Francesco, 2016. "Identification and real-time forecasting of Norwegian business cycles," International Journal of Forecasting, Elsevier, vol. 32(2), pages 283-292.
    2. Sergey V. Smirnov & Nikolay V. Kondrashov & Anna V. Petronevich, 2017. "Dating Cyclical Turning Points for Russia: Formal Methods and Informal Choices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 53-73, May.
    3. Tan, Zhengxun & Liu, Juan & Chen, Juanjuan, 2021. "Detecting stock market turning points using wavelet leaders method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    4. Grigoraş, Veaceslav & Stanciu, Irina Eusignia, 2016. "New evidence on the (de)synchronisation of business cycles: Reshaping the European business cycle," International Economics, Elsevier, vol. 147(C), pages 27-52.
    5. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
    6. Pawel Dlotko & Simon Rudkin, 2019. "The Topology of Time Series: Improving Recession Forecasting from Yield Spreads," Working Papers 2019-02, Swansea University, School of Management.
    7. Aastveit, Knut Are & Anundsen, André K. & Herstad, Eyo I., 2019. "Residential investment and recession predictability," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1790-1799.
    8. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2020. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Working Paper series 20-27, Rimini Centre for Economic Analysis.
    9. Agudze, Komla M. & Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco, 2022. "Markov switching panel with endogenous synchronization effects," Journal of Econometrics, Elsevier, vol. 230(2), pages 281-298.
    10. Gießler Stefan & Heinisch Katja & Holtemöller Oliver, 2021. "(Since When) Are East and West German Business Cycles Synchronised?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(1), pages 1-28, February.
    11. Hale, Galina & Lopez, Jose A., 2019. "Monitoring banking system connectedness with big data," Journal of Econometrics, Elsevier, vol. 212(1), pages 203-220.
    12. Hasse, Jean-Baptiste & Lajaunie, Quentin, 2022. "Does the yield curve signal recessions? New evidence from an international panel data analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 9-22.
    13. Casarin, Roberto & Costola, Michele, 2019. "Structural changes in large economic datasets: A nonparametric homogeneity test," Economics Letters, Elsevier, vol. 176(C), pages 55-59.
    14. Nissilä, Wilma, 2020. "Probit based time series models in recession forecasting – A survey with an empirical illustration for Finland," BoF Economics Review 7/2020, Bank of Finland.
    15. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    16. repec:wrk:wrkemf:37 is not listed on IDEAS
    17. Julien Chevallier & Bangzhu Zhu & Lyuyuan Zhang, 2021. "Forecasting Inflection Points: Hybrid Methods with Multiscale Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 537-575, February.
    18. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2015. "Dynamic predictive density combinations for large data sets in economics and finance," Working Paper 2015/12, Norges Bank.
    19. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2019. "Forecast density combinations with dynamic learning for large data sets in economics and finance," Working Paper 2019/7, Norges Bank.
    20. Urga, Giovanni & Wang, Fa, 2022. "Estimation and inference for high dimensional factor model with regime switching," MPRA Paper 113172, University Library of Munich, Germany.
    21. Arabinda Basistha, "undated". "Estimates of Quarterly and Monthly Episodes of Global Recessions: Evidence from Markov-switching Dynamic Factor Models," Working Papers 24-07, Department of Economics, West Virginia University.

    More about this item

    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:bla:ausecr:v:53:y:2020:i:2:p:270-291. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/mimelau.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.