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Pseudo Panel Data Models With Cohort Interactive Effects

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  • Artūras Juodis

Abstract

When genuine panel data samples are not available, repeated cross-sectional surveys can be used to form so-called pseudo panels. In this article, we investigate the properties of linear pseudo panel data estimators with fixed number of cohorts and time observations. We extend standard linear pseudo panel data setup to models with factor residuals by adapting the quasi-differencing approach developed for genuine panels. In a Monte Carlo study, we find that the proposed procedure has good finite sample properties in situations with endogeneity, cohort interactive effects, and near nonidentification. Finally, as an illustration the proposed method is applied to data from Ecuador to study labor supply elasticity. Supplementary materials for this article are available online.

Suggested Citation

  • Artūras Juodis, 2018. "Pseudo Panel Data Models With Cohort Interactive Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 47-61, January.
    • Handle: RePEc:taf:jnlbes:v:36:y:2018:i:1:p:47-61
    DOI: 10.1080/07350015.2015.1137759
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    Cited by:

    1. Arturas Juodis & Simon Reese, 2018. "The Incidental Parameters Problem in Testing for Remaining Cross-section Correlation," Papers 1810.03715, arXiv.org, revised Feb 2021.
    2. Yan Sun & Wei Huang, 2022. "Quasi-maximum likelihood estimation of short panel data models with time-varying individual effects," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 93-114, January.
    3. Juodis, Artūras & Sarafidis, Vasilis, 2022. "An incidental parameters free inference approach for panels with common shocks," Journal of Econometrics, Elsevier, vol. 229(1), pages 19-54.
    4. Rumman Khan, 2021. "Assessing Sampling Error in Pseudo‐Panel Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 742-769, June.
    5. Hai‐Anh H. Dang & Peter F. Lanjouw, 2023. "Measuring Poverty Dynamics with Synthetic Panels Based on Repeated Cross Sections," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 599-622, June.
    6. Artūras Juodis & Simas Kučinskas, 2023. "Quantifying noise in survey expectations," Quantitative Economics, Econometric Society, vol. 14(2), pages 609-650, May.
    7. Yicong Li & Qiran Zhao & Tianchang Zhai & Wei Si, 2023. "Structural transition of protein intake in urban China: Stage characteristics and driving forces," Agribusiness, John Wiley & Sons, Ltd., vol. 39(S1), pages 1559-1577, December.
    8. Shixi Kang & Jingwen Tan, 2021. "Can Education Motivate Individual Health Demands? Dynamic Pseudo-panel Evidence from China's Immigration," Papers 2112.01046, arXiv.org.

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