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Decoding economic forecasts: A novel approach to predict trends through long-term time series analysis of transportation data and GDP causal relationships Cover

Decoding economic forecasts: A novel approach to predict trends through long-term time series analysis of transportation data and GDP causal relationships

International Journal of Management and Economics
Volume 61 (2025): Issue 3 (September 2025)
By: Makoto Saito,  Tetsuaki Oda,  Kiminori Gemba and  Keiko Kubota  
Open Access
|Feb 2025

Authors

Makoto Saito

benchmark@ymail.ne.jp

Graduate School of Technology Management, Ritsumeikan University, Osaka, Japan

Tetsuaki Oda

Graduate School of Technology Management, Ritsumeikan University, Osaka, Japan

Kiminori Gemba

Business School of Innovation Management, Hosei University, Tokyo, Japan

Keiko Kubota

Abeam Consulting (USA), Inc., Chicago, USA
DOI: https://doi.org/10.2478/ijme-2024-0038 | Journal eISSN: 2543-5361 | Journal ISSN: 2299-9701
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Language: English
Page range: 174 - 186
Submitted on: Apr 27, 2024
Accepted on: Oct 12, 2024
Published on: Feb 14, 2025
Published by: Warsaw School of Economics
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
causality,
correlation analysis,
logistics data,
prediction of economic growth
Related subjects:
Business and economics,
Political economics,
Economic theory, systems and structures,
Business management,
Management accounting, financial controlling, cost calculation, investment

© 2025 Makoto Saito, Tetsuaki Oda, Kiminori Gemba, Keiko Kubota, published by Warsaw School of Economics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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