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

Saito, Makoto; Oda, Tetsuaki; Gemba, Kiminori; Kubota, Keiko

doi:10.2478/ijme-2024-0038

Abstract

This research study uses logistics data to forecast economic trends, focusing on the correlation between air and maritime transportation volumes and economic growth. It covers the top three Gross Domestic Product (GDP) countries (USA, China, and Japan) and India as a representative emerging economy. It analyzes annual data from 1974 to 2021 for GDP and air transport volume and from 2000 to 2021 for maritime transport volume. The findings reveal a causal relationship between air transport and GDP in the mentioned countries, with significant practical implications for business and policy planning, providing the reader with actionable insights and a deeper understanding of economic trends.

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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

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