IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i5p678-d1345959.html
   My bibliography  Save this article

Addressing Concerns about Single Path Analysis in Business Cycle Turning Points: The Case of Learning Vector Quantization

Author

Listed:
  • David Enck

    (Department of Industrial Manufacturing & Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA)

  • Mario Beruvides

    (Department of Industrial Manufacturing & Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA)

  • Víctor G. Tercero-Gómez

    (School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico)

  • Alvaro E. Cordero-Franco

    (Facultad de Ciencias Físico Matemáticas, Universidad Autónoma de Nuevo León, San Nicolás de los Garza 66451, Mexico)

Abstract

Data-driven approaches in machine learning are increasingly applied in economic analysis, particularly for identifying business cycle (BC) turning points. However, temporal dependence in BCs is often overlooked, leading to what we term single path analysis (SPA). SPA neglects the diverse potential routes of a temporal data structure. It hinders the evaluation and calibration of algorithms. This study emphasizes the significance of acknowledging temporal dependence in BC analysis and illustrates the problem of SPA using learning vector quantization (LVQ) as a case study. LVQ was previously adapted to use economic indicators to determine the current BC phase, exhibiting flexibility in adapting to evolving patterns. To address temporal complexities, we employed a multivariate Monte Carlo simulation incorporating a specified number of change-points, autocorrelation, and cross-correlations, from a second-order vector autoregressive model. Calibrated with varying levels of observed economic leading indicators, our approach offers a deeper understanding of LVQ’s uncertainties. Our results demonstrate the inadequacy of SPA, unveiling diverse risks and worst-case protection strategies. By encouraging researchers to consider temporal dependence, this study contributes to enhancing the robustness of data-driven approaches in financial and economic analyses, offering a comprehensive framework for addressing SPA concerns.

Suggested Citation

  • David Enck & Mario Beruvides & Víctor G. Tercero-Gómez & Alvaro E. Cordero-Franco, 2024. "Addressing Concerns about Single Path Analysis in Business Cycle Turning Points: The Case of Learning Vector Quantization," Mathematics, MDPI, vol. 12(5), pages 1-15, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:678-:d:1345959
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/5/678/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/5/678/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Barış Soybilgen, 2020. "Identifying US business cycle regimes using dynamic factors and neural network models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 827-840, August.
    2. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    3. Hamilton, James D., 2011. "Calling recessions in real time," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1006-1026, October.
    4. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-996, November.
    5. Goodwin, Thomas H, 1993. "Business-Cycle Analysis with a Markov-Switching Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 331-339, July.
    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. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    2. 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.
    3. 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.
    4. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    5. 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.
    6. Sun, Jiandong & Feng, Shuaizhang & Hu, Yingyao, 2021. "Misclassification errors in labor force statuses and the early identification of economic recessions," Journal of Asian Economics, Elsevier, vol. 75(C).
    7. 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.
    8. Camacho, Maximo & Perez-Quiros, Gabriel & Poncela, Pilar, 2018. "Markov-switching dynamic factor models in real time," International Journal of Forecasting, Elsevier, vol. 34(4), pages 598-611.
    9. Maximo Camacho & Gabriel Perez‐Quiros & Pilar Poncela, 2015. "Extracting Nonlinear Signals from Several Economic Indicators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1073-1089, November.
    10. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2015. "Markov-switching mixed-frequency VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 692-711.
    11. Rachidi Kotchoni & Dalibor Stevanovic, 2016. "Forecasting U.S. Recessions and Economic Activity," Working Papers hal-04141569, HAL.
    12. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
    13. Pauwels, Laurent & Vasnev, Andrey, 2014. "Forecast combination for U.S. recessions with real-time data," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 138-148.
    14. Marco Rubilar-González & Gabriel Pino, 2018. "Are Euro-Area expectations about recession phases effective to anticipate consequences of economic crises?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 9(2), pages 141-161, June.
    15. Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2020. "Business cycle dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model," Working Papers halshs-02443364, HAL.
    16. Knut Are Aastveit & Anne Sofie Jore & Francesco Ravazzolo, 2014. "Forecasting recessions in real time," Working Paper 2014/02, Norges Bank.
    17. Al-Anaswah, Nael & Wilfling, Bernd, 2011. "Identification of speculative bubbles using state-space models with Markov-switching," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1073-1086, May.
    18. Romain Aumond & Julien Royer, 2024. "Improving the robustness of Markov-switching dynamic factor models with time-varying volatility," Working Papers 2024-04, Center for Research in Economics and Statistics.
    19. repec:syb:wpbsba:05/2013 is not listed on IDEAS
    20. Hamilton, J.D., 2016. "Macroeconomic Regimes and Regime Shifts," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 163-201, Elsevier.
    21. Ozdemir Dicle, 2020. "Time-Varying Housing Market Fluctuations: Evidence from the U.S. Housing Market," Real Estate Management and Valuation, Sciendo, vol. 28(2), pages 89-99, June.

    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:12:y:2024:i:5:p:678-:d:1345959. 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.