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Transparency in Structural Research

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  • Isaiah Andrews
  • Matthew Gentzkow
  • Jesse M. Shapiro

Abstract

We propose a formal definition of transparency in empirical research and apply it to structural estimation in economics. We discuss how some existing practices can be understood as attempts to improve transparency, and we suggest ways to improve current practice, emphasizing approaches that impose a minimal computational burden on the researcher. We illustrate with examples.

Suggested Citation

  • Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2020. "Transparency in Structural Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 711-722, October.
    • Handle: RePEc:taf:jnlbes:v:38:y:2020:i:4:p:711-722
    DOI: 10.1080/07350015.2020.1796395
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    Cited by:

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    2. Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2020. "Reply to: Comments on “On the Informativeness of Descriptive Statistics for Structural Estimates”," Econometrica, Econometric Society, vol. 88(6), pages 2277-2279, November.
    3. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
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    5. Marco Stenborg Petterson & David Seim & Jesse M. Shapiro, 2023. "Bounds on a Slope from Size Restrictions on Economic Shocks," American Economic Journal: Microeconomics, American Economic Association, vol. 15(3), pages 552-572, August.
    6. Roberto Dell’Anno, 2023. "Measuring the unobservable: estimating informal economy by a structural equation modeling approach," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(1), pages 247-277, February.

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