GDP responses to supply chain disruptions in a post-pandemic era: Combination of DL and ANN outputs based on Google Trends

Umer Shahzad, Kamel Si Mohammed, Nicolas Schneider, Francesca Faggioni and Armando Papa

Technological Forecasting and Social Change, 2023, vol. 192, issue C

Abstract: With the recent Russian-Ukraine conflict, the frequency and intensity of disruptive shocks on major supply chains have risen, causing increasing food and energy security concerns for regulators. That is, the combination of newly available sophisticated deep learning tools with real-time series data may represent a fruitful policy direction because machines can identify patterns without being pre-conditioned calibration thanks to experimental data training. This paper employs Deep Learning (DL) and Artificial Neural Network (ANN) algorithms and aimed predicts GDP responses to supply chain disruptions, energy prices, economic policy uncertainty, and google trend in the US. Sampled data from 2008 to 2022 are monthly wrangled and embed different recession episodes connected to the subprime crisis of 2008, the COVID-19 pandemic, the recent invasion of Ukraine by Russia, and the current economic recession in the US. Both DL and ANN outputs empirically (and unanimously) demonstrated how sensitive monthly GDP variations are to dynamic changes in supply chain performances. Findings identify the substantial role of google trends in delivering a consistent fit to predicted GDP values, which has implications While a comparative discussion over the larger forecasting performance of DL compared to ANN experiments is offered, implications for global policy, decision-makers and firm managers are finally provided.

Keywords: Supply chain; Economic growth; Deep learning; Artificial Neural Network; United States; Google trends (search for similar items in EconPapers)
JEL-codes: L91 L92 N70 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:192:y:2023:i:c:s004016252300197x

DOI: 10.1016/j.techfore.2023.122512

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