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Score-driven latent-factor panel data models of economic freedom: an empirical application to the United States

Author

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  • Szabolcs Blazsek
  • Andrés Marroquín
  • Zachary A. Thomas
  • C. Asa Lambert

Abstract

In this paper, we study the link between economic freedom and gross domestic product (GDP) growth of 12 industries making up the United States (US) economy for 50 US states from 2005 to 2020. To measure the industry-specific impact of economic freedom in the US, we use a novel panel data model, named the score-driven latent-factor panel data model of economic freedom, which includes US state- and industry-specific score-driven components, US state- and industry-specific unobserved effects, and federal-level latent factor. We show that the statistical performance of the novel panel data model is superior to those of classical static and dynamic panel data models. With the exception of the ‘Agriculture’ and ‘Utilities’ industries, we find a positive relationship between economic freedom and growth in 10 of the 12 US industries considered for the score-driven latent-factor panel data model.

Suggested Citation

  • Szabolcs Blazsek & Andrés Marroquín & Zachary A. Thomas & C. Asa Lambert, 2025. "Score-driven latent-factor panel data models of economic freedom: an empirical application to the United States," Applied Economics, Taylor & Francis Journals, vol. 57(30), pages 4263-4278, June.
    • Handle: RePEc:taf:applec:v:57:y:2025:i:30:p:4263-4278
    DOI: 10.1080/00036846.2024.2354515
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