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Packing Peanuts: The Role Synthetic Data Can Play in Enhancing Conventional Economic Prediction Models

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  • Vansh Murad Kalia

Abstract

Packing peanuts, as defined by Wikipedia, is a common loose-fill packaging and cushioning material that helps prevent damage to fragile items. In this paper, I propose that synthetic data, akin to packing peanuts, can serve as a valuable asset for economic prediction models, enhancing their performance and robustness when integrated with real data. This hybrid approach proves particularly beneficial in scenarios where data is either missing or limited in availability. Through the utilization of Affinity credit card spending and Womply small business datasets, this study demonstrates the substantial performance improvements achieved by employing a hybrid data approach, surpassing the capabilities of traditional economic modeling techniques.

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  • Vansh Murad Kalia, 2024. "Packing Peanuts: The Role Synthetic Data Can Play in Enhancing Conventional Economic Prediction Models," Papers 2405.07431, arXiv.org.
    • Handle: RePEc:arx:papers:2405.07431
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    References listed on IDEAS

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    1. Allison Koenecke & Hal Varian, 2020. "Synthetic Data Generation for Economists," Papers 2011.01374, arXiv.org, revised Nov 2020.
    2. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
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