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Semiparametric Identification in Panel Data Discrete Response Models

Author

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  • Eleni (E.) Aristodemou

    (University of Amsterdam, The Netherlands)

Abstract

This paper studies semiparametric identification in linear index discrete response panel data models with fixed effects. Departing from the classic binary response static panel data model, this paper examines identification in the binary response dynamic panel data model and the ordered response static panel data model. It is shown that under mild distributional assumptions on the fixed effect and the time-varying unobservables, point-identification fails but informative bounds on the regression coefficients can still be derived. Partial identification is achieved by eliminating the fixed effect and discovering features of the distribution of the unobservable time-varying components that do not depend on the unobserved heterogeneity. Numerical analyses illustrate how the identified set changes as the support of the explanatory variables varies.

Suggested Citation

  • Eleni (E.) Aristodemou, 2018. "Semiparametric Identification in Panel Data Discrete Response Models," Tinbergen Institute Discussion Papers 18-065/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20180065
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    References listed on IDEAS

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    7. Irene Botosaru & Chris Muris, 2017. "Binarization for panel models with fixed effects," CeMMAP working papers 31/17, Institute for Fiscal Studies.
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    Citations

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    Cited by:

    1. Botosaru, Irene & Muris, Chris & Pendakur, Krishna, 2023. "Identification of time-varying transformation models with fixed effects, with an application to unobserved heterogeneity in resource shares," Journal of Econometrics, Elsevier, vol. 232(2), pages 576-597.
    2. Chris Muris & Pedro Raposo & Sotiris Vandoros, 2020. "A dynamic ordered logit model with fixed effects," Papers 2008.05517, arXiv.org.
    3. Bo E Honoré & Áureo de Paula, 2021. "Identification in simple binary outcome panel data models," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 78-93.
    4. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    5. 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.
    6. Irene Botosaru & Chris Muris & Krishna Pendakur, 2020. "Intertemporal Collective Household Models: Identification in Short Panels with Unobserved Heterogeneity in Resource Shares," CeMMAP working papers CWP26/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Bo E. Honor'e & Martin Weidner, 2020. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," Papers 2005.05942, arXiv.org, revised Dec 2023.
    8. Bo E. Honoré & Martin Weidner, 2021. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," Working Papers 2021-79, Princeton University. Economics Department..
    9. Bo E. Honoré & Martin Weidner, 2020. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," CeMMAP working papers CWP38/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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    More about this item

    Keywords

    Static and Dynamic Panel Data; Binary Response Models; Ordered Response Models; Semiparametric Identification; Partial Identification;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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