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Estimation of Dynamic Nonlinear Random Effects Models with Unbalanced Panels

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  • Carro, Jesús M.
  • Albarrán, Pedro
  • Carrasco, Raquel

Abstract

This paper presents and evaluates estimation methods for dynamic non-linear models with correlated random effects (CRE) when we have unbalanced panels. Accounting for the unbalancedness is crucial in dynamic non-linear models and ignoring it produces inconsistent estimates of the parameters even if the process that drives it 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. In this paper we allow the sample selection process that determines the unbalancedness structure of the data to be arbitrarily 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 our proposed estimation approaches perform better both in terms of bias and RMSE than the approaches that ignore the unbalancedness or that balance the sample.

Suggested Citation

  • Carro, Jesús M. & Albarrán, Pedro & Carrasco, Raquel, 2015. "Estimation of Dynamic Nonlinear Random Effects Models with Unbalanced Panels," UC3M Working papers. Economics we1503, Universidad Carlos III de Madrid. Departamento de Economía.
  • Handle: RePEc:cte:werepe:we1503
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    Cited by:

    1. Chris Muris & Pedro Raposo & Sotiris Vandoros, 2020. "A Dynamic Ordered Logit Model with Fixed Effects," Department of Economics Working Papers 2020-14, McMaster University.
    2. 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.
    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. 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.
    6. 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.

    More about this item

    Keywords

    Unbalanced panels;

    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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