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

TL;DR

This paper explores how synthetic data, likened to packing peanuts, can improve economic prediction models by enhancing their robustness and performance, especially when real data is scarce or incomplete.

Contribution

It introduces a hybrid data approach that combines synthetic and real data to significantly boost the accuracy of economic models.

Findings

Hybrid data approach outperforms traditional models.

Synthetic data improves robustness in data-limited scenarios.

Demonstrated using credit card and small business datasets.

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.