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Feedback in Panel Data Medels

Author

Listed:
  • Chamberlain, G.

Abstract

Much of the analysis of panel data has been based on an assumption of strict exogeneity. Distributions are specified for outcome variables conditional on a latent individual effect and conditional on observed predictor variables at all dates, with the future values of the predictor variables assumed to have no effect on the conditional distribution. The paper relaxes this assumption in order to allow for lagged dependent variables and, more generally, for feedback from lagged dependent variables to current values of the predictor variables. Such feedback would arise in an evaluation study if the treatment variable is randomly assigned only conditional on the individual effect and on previous outcomes.
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Suggested Citation

  • Chamberlain, G., 1993. "Feedback in Panel Data Medels," Harvard Institute of Economic Research Working Papers 1656, Harvard - Institute of Economic Research.
    • Handle: RePEc:fth:harver:1656
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    Cited by:

    1. Geert Dhaene & Koen Jochmans, 2011. "Profile-score Adjustements for Nonlinearfixed-effect Models," Working Papers hal-01073733, HAL.
    2. Vasilis Sarafidis & Tom Wansbeek, 2020. "Celebrating 40 Years of Panel Data Analysis: Past, Present and Future," Monash Econometrics and Business Statistics Working Papers 6/20, Monash University, Department of Econometrics and Business Statistics.
    3. Athey, Susan & Imbens, Guido W., 2022. "Design-based analysis in Difference-In-Differences settings with staggered adoption," Journal of Econometrics, Elsevier, vol. 226(1), pages 62-79.
    4. Moaniba, Igam M. & Su, Hsin-Ning & Lee, Pei-Chun, 2019. "On the drivers of innovation: Does the co-evolution of technological diversification and international collaboration matter?," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    5. Peter H. Egger & Christoph Jessberger & Mario Larch, 2013. "Impacts of Trade and the Environment on Clustered Multilateral Environmental Agreements," The World Economy, Wiley Blackwell, vol. 36(3), pages 331-348, March.
    6. Francesco Bartolucci & Francesco Valentini & Claudia Pigini, 2023. "Recursive Computation of the Conditional Probability Function of the Quadratic Exponential Model for Binary Panel Data," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 529-557, February.
    7. repec:spo:wpmain:info:hdl:2441/eu4vqp9ompqllr09j0031f620 is not listed on IDEAS
    8. Ai, Chunrong & Gan, Li, 2010. "An alternative root-n consistent estimator for panel data binary choice models," Journal of Econometrics, Elsevier, vol. 157(1), pages 93-100, July.
    9. Stéphane Bonhomme & Kevin Dano & Bryan S. Graham, 2023. "Identification in a binary choice panel data model with a predetermined covariate," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 14(3), pages 315-351, December.
    10. Francesco Bartolucci & Valentina Nigro & Claudia Pigini, 2018. "Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model," Econometric Reviews, Taylor & Francis Journals, vol. 37(1), pages 61-88, January.
    11. Geert Dhaene & Koen Jochmans, 2011. "Profile-score Adjustements for Nonlinearfixed-effect Models," Working Papers hal-01073733, HAL.
    12. Bartolucci, Francesco & Nigro, Valentina & Pigini, Claudia, 2013. "Testing for state dependence in binary panel data with individual covariates," MPRA Paper 48233, University Library of Munich, Germany.
    13. St'ephane Bonhomme & Kevin Dano & Bryan S. Graham, 2025. "Moment Restrictions for Nonlinear Panel Data Models with Feedback," Papers 2506.12569, arXiv.org, revised Jul 2025.
    14. Su, Hsin-Ning & Moaniba, Igam M., 2017. "Does innovation respond to climate change? Empirical evidence from patents and greenhouse gas emissions," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 49-62.
    15. Anish Agarwal & Sukjin Han & Dwaipayan Saha & Vasilis Syrgkanis & Haeyeon Yoon, 2022. "Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects," Papers 2210.11003, arXiv.org, revised Oct 2025.
    16. Manuel Arellano & Stéphane Bonhomme, 2012. "Identifying Distributional Characteristics in Random Coefficients Panel Data Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 987-1020.
    17. repec:spo:wpecon:info:hdl:2441/eu4vqp9ompqllr09j0031f620 is not listed on IDEAS
    18. Kenneth Y. Chay & Michael Greenstone, 2005. "Does Air Quality Matter? Evidence from the Housing Market," Journal of Political Economy, University of Chicago Press, vol. 113(2), pages 376-424, April.
    19. St'ephane Bonhomme & Kevin Dano & Bryan S. Graham, 2023. "Identification in a Binary Choice Panel Data Model with a Predetermined Covariate," Papers 2301.05733, arXiv.org, revised Jul 2023.
    20. Stéphane Bonhomme & Kevin Dano & Bryan S. Graham, 2025. "Moment restrictions for nonlinear panel data models with feedback," CeMMAP working papers 12/25, Institute for Fiscal Studies.
    21. Changbiao Liu, 2024. "Estimating dynamic logit models with unobserved individual heterogeneity and with application in household brand choices," Computational and Mathematical Organization Theory, Springer, vol. 30(4), pages 321-349, December.
    22. Stéphane Bonhomme & Kevin Dano & Bryan S. Graham, 2023. "Identification in a binary choice panel data model with a predetermined covariate," CeMMAP working papers 17/23, Institute for Fiscal Studies.
    23. Langevin, R.;, 2024. "Consistent Estimation of Finite Mixtures: An Application to Latent Group Panel Structures," Health, Econometrics and Data Group (HEDG) Working Papers 24/16, HEDG, c/o Department of Economics, University of York.

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