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Nonlinear Panel Data Analysis

Author

Listed:
  • Manuel Arellano
  • Stèphane Bonhomme

    () (CEMFI, 28014 Madrid, Spain)

Abstract

Nonlinear panel data models arise naturally in economic applications, yet their analysis is challenging. Here we provide a progress report on some recent advances in the area. We start by reviewing the properties of random-effects likelihood approaches. We emphasize a link with Bayesian computation and Markov chain Monte Carlo, which provides a convenient approach to estimation and inference. The relaxation of parametric assumptions on the distribution of individual effects raises serious identification problems. In discrete choice models, common parameters and average marginal effects are generally set identified. The availability of continuous outcomes, however, provides opportunities for point identification. We end by reviewing recent progress on non-fixed-T approaches. In panel applications in which the time dimension is not negligible relative to the size of the cross section, it makes sense to view the estimation problem as a time-series finite-sample bias. Several perspectives to bias reduction are now available. We review their properties, with a special emphasis on random-effects methods.

Suggested Citation

  • Manuel Arellano & Stèphane Bonhomme, 2011. "Nonlinear Panel Data Analysis," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 395-424, September.
  • Handle: RePEc:anr:reveco:v:3:y:2011:p:395-424
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    File URL: http://www.annualreviews.org/doi/abs/10.1146/annurev-economics-111809-125139
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    Cited by:

    1. Dubois, Pierre & Griffith, Rachel & O'Connell, Martin, 2017. "How well targeted are soda taxes?," TSE Working Papers 17-868, Toulouse School of Economics (TSE), revised Jan 2019.
    2. repec:wly:emjrnl:v:19:y:2016:i:3:p:c61-c94 is not listed on IDEAS
    3. repec:eee:empfin:v:51:y:2019:i:c:p:17-27 is not listed on IDEAS
    4. Santiago Pereda Fernández, 2016. "Copula-based random effects models for clustered data," Temi di discussione (Economic working papers) 1092, Bank of Italy, Economic Research and International Relations Area.
    5. repec:eee:econom:v:206:y:2018:i:2:p:305-335 is not listed on IDEAS
    6. repec:spr:scient:v:96:y:2013:i:1:d:10.1007_s11192-013-0954-3 is not listed on IDEAS
    7. Fabienne Femenia & Alain Carpentier & Obafemi Philippe Koutchade, 2018. "Dealing with corner solutions in multi-crop micro-econometric models: an endogenous regime approach with regime fixed costs," Post-Print hal-01879042, HAL.
    8. Mesters, G. & Koopman, S.J., 2014. "Generalized dynamic panel data models with random effects for cross-section and time," Journal of Econometrics, Elsevier, vol. 180(2), pages 127-140.
    9. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data estimation via quantile regressions," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 61-94, October.
    10. Andrew Adrian Yu Pua, 2015. "On IV estimation of a dynamic linear probability model with fixed effects," UvA-Econometrics Working Papers 15-01, Universiteit van Amsterdam, Dept. of Econometrics.
    11. repec:oup:restud:v:85:y:2018:i:4:p:2429-2461. is not listed on IDEAS
    12. José-Alberto Guerra & Myra Mohnen, 2017. "Multinomial choice with social interactions: occupations in Victorian London," Documentos CEDE 015667, Universidad de los Andes - CEDE.
    13. Graham, Bryan S. & Hahn, Jinyong & Poirier, Alexandre & Powell, James L., 2018. "A quantile correlated random coefficients panel data model," Journal of Econometrics, Elsevier, vol. 206(2), pages 305-335.
    14. Majid M. Al-Sadoon & Tong Li & M. Hashem Pesaran, 2017. "Exponential class of dynamic binary choice panel data models with fixed effects," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 898-927, October.
    15. Alessandra Casarico & Paola Profeta & Chiara Daniela Pronzato, 2016. "On the Regional Labour Market Determinants of Female University Enrolment in Europe," Regional Studies, Taylor & Francis Journals, vol. 50(6), pages 1036-1053, June.
    16. Mark J Roberts & Daniel Yi Xu & Xiaoyan Fan & Shengxing Zhang, 2018. "The Role of Firm Factors in Demand, Cost, and Export Market Selection for Chinese Footwear Producers," Review of Economic Studies, Oxford University Press, vol. 85(4), pages 2429-2461.
    17. Min Deng & Wentao Yang & Qiliang Liu & Yunfei Zhang, 2017. "A divide-and-conquer method for space–time series prediction," Journal of Geographical Systems, Springer, vol. 19(1), pages 1-19, January.
    18. Ghanem, Dalia, 2017. "Testing identifying assumptions in nonseparable panel data models," Journal of Econometrics, Elsevier, vol. 197(2), pages 202-217.
    19. Daniel Yi Xu & Mark Roberts, 2012. "A Structural Model of Dmand, Cost, and Export Market Selection for Chinese Footwear Producers," 2012 Meeting Papers 294, Society for Economic Dynamics.
    20. Bartolucci, Francesco & Belotti, Federico & Peracchi, Franco, 2015. "Testing for time-invariant unobserved heterogeneity in generalized linear models for panel data," Journal of Econometrics, Elsevier, vol. 184(1), pages 111-123.

    More about this item

    Keywords

    incidental parameters; unobserved heterogeneity;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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