IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v56y2020i1d10.1007_s10614-020-09986-0.html
   My bibliography  Save this article

Intertemporal Similarity of Economic Time Series: An Application of Dynamic Time Warping

Author

Listed:
  • Philip Hans Franses

    (Erasmus School of Economics)

  • Thomas Wiemann

    (Erasmus School of Economics)

Abstract

This paper adapts the non-parametric dynamic time warping (DTW) technique in an application to examine the temporal alignment and similarity across economic time series. DTW has important advantages over existing measures in economics as it alleviates concerns regarding a pre-defined fixed temporal alignment of series. For example, in contrast to current methods, DTW can capture alternations between leading and lagging relationships of series. We illustrate DTW in a study of US states’ business cycles around the Great Recession, and find considerable evidence that temporal alignments across states dynamic. Trough cluster analysis, we further document state-varying recoveries from the recession.

Suggested Citation

  • Philip Hans Franses & Thomas Wiemann, 2020. "Intertemporal Similarity of Economic Time Series: An Application of Dynamic Time Warping," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 59-75, June.
    • Handle: RePEc:kap:compec:v:56:y:2020:i:1:d:10.1007_s10614-020-09986-0
    DOI: 10.1007/s10614-020-09986-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-020-09986-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-020-09986-0?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(4), pages 369-380, October.
    2. Christos Katris, 2020. "Prediction of Unemployment Rates with Time Series and Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 673-706, February.
    3. Francisco Corona & Pilar Poncela & Esther Ruiz, 2020. "Estimating Non-stationary Common Factors: Implications for Risk Sharing," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 37-60, January.
    4. Theodore M. Crone, 2005. "An Alternative Definition of Economic Regions in the United States Based on Similarities in State Business Cycles," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 617-626, November.
    5. Neville Francis & Michael T. Owyang & Ozge Savascin, 2017. "An endogenously clustered factor approach to international business cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1261-1276, November.
    6. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    7. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(4), pages 393-395, October.
    8. Papageorgiou, Theofanis & Michaelides, Panayotis G. & Milios, John G., 2010. "Business cycles synchronization and clustering in Europe (1960-2009)," Journal of Economics and Business, Elsevier, vol. 62(5), pages 419-470, September.
    9. James D. Hamilton & Michael T. Owyang, 2012. "The Propagation of Regional Recessions," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 935-947, November.
    10. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2009. "Studying co-movements in large multivariate data prior to multivariate modelling," Journal of Econometrics, Elsevier, vol. 148(1), pages 25-35, January.
    11. Fruhwirth-Schnatter, Sylvia & Kaufmann, Sylvia, 2008. "Model-Based Clustering of Multiple Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 78-89, January.
    12. Sullivan, Mary Kay & Miller, Alex, 1996. "Segmenting the informal venture capital market: Economic, hedonistic, and altruistic investors," Journal of Business Research, Elsevier, vol. 36(1), pages 25-35, May.
    13. Funashima, Yoshito, 2017. "Time-varying leads and lags across frequencies using a continuous wavelet transform approach," Economic Modelling, Elsevier, vol. 60(C), pages 24-28.
    14. Wang, Gang-Jin & Xie, Chi & Han, Feng & Sun, Bo, 2012. "Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4136-4146.
    15. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    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. Philip Hans Franses, 2021. "Modeling Judgment in Macroeconomic Forecasts," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 401-417, December.
    2. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
    3. Mattera, Raffaele & Franses, Philip Hans, 2023. "Are African business cycles synchronized? Evidence from spatio-temporal modeling," Economic Modelling, Elsevier, vol. 128(C).
    4. Tatsuru Kikuchi & Toranosuke Onishi & Kenichi Ueda, 2021. "Price Stability of Cryptocurrencies as a Medium of Exchange," CARF F-Series CARF-F-526, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.

    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. Franses, Ph.H.B.F. & Wiemann, T., 2018. "Intertemporal Similarity of Economic Time Series," Econometric Institute Research Papers EI2018-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Franses, Ph.H.B.F., 2019. "Do African economies grow similarly?," Econometric Institute Research Papers EI2019-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Neville Francis & Michael T. Owyang & Ozge Savascin, 2017. "An endogenously clustered factor approach to international business cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1261-1276, November.
    4. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2021. "Spurious relationships in high-dimensional systems with strong or mild persistence," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1480-1497.
    5. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Forecasting with factor-augmented error correction models," International Journal of Forecasting, Elsevier, vol. 30(3), pages 589-612.
    6. Marco Centoni & Gianluca Cubadda, 2011. "Modelling comovements of economic time series: a selective survey," Statistica, Department of Statistics, University of Bologna, vol. 71(2), pages 267-294.
    7. Coroneo, Laura & Jackson, Laura E. & Owyang, Michael T., 2020. "International Stock Comovements with Endogenous Clusters," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    8. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Forecasting with factor-augmented error correction models," International Journal of Forecasting, Elsevier, vol. 30(3), pages 589-612.
    9. Matteo Lanzafame, 2010. "The nature of regional unemployment in Italy," Empirical Economics, Springer, vol. 39(3), pages 877-895, December.
    10. Sergei S. Shibaev, 2016. "Recession Propagation In Small Regional Economies: Spatial Spillovers And Endogenous Clustering," Working Paper 1369, Economics Department, Queen's University.
    11. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    12. Apostolos Serletis & Ricardo Rangel-Ruiz, 2007. "Testing for Common Features in North American Energy Markets," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 14, pages 172-187, World Scientific Publishing Co. Pte. Ltd..
    13. Mamingi Nlandu, 2017. "Beauty and Ugliness of Aggregation over Time: A Survey," Review of Economics, De Gruyter, vol. 68(3), pages 205-227, December.
    14. Osmani Teixeira de Carvalho de Guillén & Carlos Hamilton Vasconcelos Araújo, 2005. "O Mecanismo De Transmissão Da Taxa De Câmbio Para Índices De Preços: Uma Análise Vecm Para O Brasil," Anais do XXXIII Encontro Nacional de Economia [Proceedings of the 33rd Brazilian Economics Meeting] 034, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    15. Jinghui Chen & Masahito Kobayashi & Michael McAleer, 2017. "Testing for volatility co-movement in bivariate stochastic volatility models," Documentos de Trabajo del ICAE 2017-10, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    16. Lafuente, Juan A. & Novales, Alfonso, 2003. "Optimal hedging under departures from the cost-of-carry valuation: Evidence from the Spanish stock index futures market," Journal of Banking & Finance, Elsevier, vol. 27(6), pages 1053-1078, June.
    17. Gaia Garino & Lucio Sarno, 2004. "Speculative Bubbles in U.K. House Prices: Some New Evidence," Southern Economic Journal, John Wiley & Sons, vol. 70(4), pages 777-795, April.
    18. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2009. "Studying co-movements in large multivariate data prior to multivariate modelling," Journal of Econometrics, Elsevier, vol. 148(1), pages 25-35, January.
    19. Chen, Xiaoshan & Mills, Terence C., 2009. "Evaluating growth cycle synchronisation in the EU," Economic Modelling, Elsevier, vol. 26(2), pages 342-351, March.
    20. Sharon Kozicki & Peter A. Tinsley, "undated". "Moving Endpoints in Macrofinance," Computing in Economics and Finance 1996 _058, Society for Computational Economics.

    More about this item

    Keywords

    Business cycles; Non-parametric method; Dynamic time warping;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:kap:compec:v:56:y:2020:i:1:d:10.1007_s10614-020-09986-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.