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Using Stata to estimate dynamic correlated random effectsprobit models with unbalanced panels

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Abstract

This paper implements the estimation of dynamic probit correlated random effects (CRE) models with unbalanced panel data. The type of models we consider include a lag of the endogenous variable and other explanatory variables that are strictly exogenous. We introduce a Stata package, xtprobitunbal; this command estimates these models allowing for the unbalancedness process to be correlated with the time-invariant unobserved heterogeneity. It reduces the computational burden of the maximum likelihood (ML) estimation, while keeping its good asymptotic properties.We also introduce the command mgf_unbal to compute the marginal effects ofthe variables of the model and its standard errors. Finally, we study the estimation of CRE unbalanced panel data probit models by ML estimation and under more restrictive assumptions than the ones considered by xtprobitunbal, discussing the main problems to implement them.

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  • Albarrán, Pedro, 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.
    • Handle: RePEc:cte:werepe:30116
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    1. Rabe-Hesketh, Sophia & Skrondal, Anders, 2013. "Avoiding biased versions of Wooldridge’s simple solution to the initial conditions problem," Economics Letters, Elsevier, vol. 120(2), pages 346-349.
    2. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54, January.
    3. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
    4. Wiji Arulampalam & Mark B. Stewart, 2009. "Simplified Implementation of the Heckman Estimator of the Dynamic Probit Model and a Comparison with Alternative Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 659-681, October.
    5. 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.
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    Cited by:

    1. Corradini, Carlo & D'Ippolito, Beatrice, 2022. "Persistence and learning effects in design innovation: Evidence from panel data," Research Policy, Elsevier, vol. 51(2).

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    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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