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.The authors gratefully acknowledge research funding from the Spanish Ministry of Education, Grants ECO2012-31358 and ECO2015-65204-P
