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Synthetic Data Generation for Economists

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  • Allison Koenecke
  • Hal Varian

Abstract

As more tech companies engage in rigorous economic analyses, we are confronted with a data problem: in-house papers cannot be replicated due to use of sensitive, proprietary, or private data. Readers are left to assume that the obscured true data (e.g., internal Google information) indeed produced the results given, or they must seek out comparable public-facing data (e.g., Google Trends) that yield similar results. One way to ameliorate this reproducibility issue is to have researchers release synthetic datasets based on their true data; this allows external parties to replicate an internal researcher's methodology. In this brief overview, we explore synthetic data generation at a high level for economic analyses.

Suggested Citation

  • Allison Koenecke & Hal Varian, 2020. "Synthetic Data Generation for Economists," Papers 2011.01374, arXiv.org, revised Nov 2020.
    • Handle: RePEc:arx:papers:2011.01374
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    References listed on IDEAS

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    1. Susan Athey & Guido Imbens & Jonas Metzger & Evan Munro, 2019. "Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations," Papers 1909.02210, arXiv.org, revised Jul 2020.
    2. Seth Stephens-Davidowitz & Hal Varian & Michael D. Smith, 2017. "Super returns to Super Bowl ads?," Quantitative Marketing and Economics (QME), Springer, vol. 15(1), pages 1-28, March.
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    Cited by:

    1. Yves-C'edric Bauwelinckx & Jan Dhaene & Tim Verdonck & Milan van den Heuvel, 2023. "On the causality-preservation capabilities of generative modelling," Papers 2301.01109, arXiv.org.
    2. Sonan Memon, 2022. "Inflation in Pakistan: High-Frequency Estimation and Forecasting," PIDE-Working Papers 2022:12, Pakistan Institute of Development Economics.
    3. Stefan Wimmer & Robert Finger, 2023. "A note on synthetic data for replication purposes in agricultural economics," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(1), pages 316-323, February.

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