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Estimation of Dynamic Nonlinear Random Effects Models with Unbalanced Panels

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  • Pedro Albarran
  • Raquel Carrasco
  • Jesus M. Carro

Abstract

This paper presents estimation methods for dynamic nonlinear models with correlated random effects (CRE) when having unbalanced panels. Unbalancedness is often encountered in applied work and ignoring it in dynamic nonlinear models produces inconsistent estimates even if the unbalancedness process is completely at random. We show that selecting a balanced panel from the sample can produce efficiency losses or even inconsistent estimates of the average marginal effects. We allow the process that determines the unbalancedness structure of the data to be correlated with the permanent unobserved heterogeneity. We discuss how to address the estimation by maximizing the likelihood function for the whole sample and also propose a Minimum Distance approach, which is computationally simpler and asymptotically equivalent to the Maximum Likelihood estimation. Our Monte Carlo experiments and empirical illustration show that the issue is relevant. Our proposed solutions perform better both in terms of bias and RMSE than the approaches that ignore the unbalancedness or that balance the sample.

Suggested Citation

  • Pedro Albarran & Raquel Carrasco & Jesus M. Carro, 2019. "Estimation of Dynamic Nonlinear Random Effects Models with Unbalanced Panels," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(6), pages 1424-1441, December.
  • Handle: RePEc:bla:obuest:v:81:y:2019:i:6:p:1424-1441
    DOI: 10.1111/obes.12308
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    References listed on IDEAS

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    Cited by:

    1. Chris Muris & Pedro Raposo & Sotiris Vandoros, 2020. "A dynamic ordered logit model with fixed effects," Papers 2008.05517, arXiv.org.
    2. Beusch, Elisabeth & Van Soest, Arthur, 2020. "A dynamic multinomial model of self-employment in the Netherlands," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 5-32.
    3. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    4. Chrysanthou, Georgios Marios & Guilló, María Dolores, 2018. "The dynamics of political party support and egocentric economic evaluations: The Scottish case," European Journal of Political Economy, Elsevier, vol. 52(C), pages 192-213.
    5. Carro, Jesús M. & Albarrán, Pedro & Carrasco, Raquel, 2020. "Using Stata to estimate dynamic correlated random effectsprobit models with unbalanced panels," UC3M Working papers. Economics 30116, Universidad Carlos III de Madrid. Departamento de Economía.
    6. Chrysanthou, Georgios Marios & Guilló, María Dolores, 2016. "The Dynamics of Heterogeneous Political Party Support and Egocentric Economic Evaluations: the Scottish Case," QM&ET Working Papers 16-3, University of Alicante, D. Quantitative Methods and Economic Theory.

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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