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

David Enck, Mario Beruvides, Víctor G. Tercero-Gómez and Alvaro E. Cordero-Franco ()
Additional contact information
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

Mathematics, 2024, vol. 12, issue 5, 1-15

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.

Keywords: data-driven methods; temporal dependence; Monte Carlo simulation; robustness; multivariate analysis; economic indicators (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/5/678/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/5/678/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:5:p:678-:d:1345959

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().