Abstract
As seen in the previous chapters, estimation of nonlinear models frequently involves integration with respect to the heterogeneity distribution. Procedures for the estimation of a large class of models involving specific heterogeneity distributions are widely available. Anderson and Aitkin [1985], Im and Gianola [1988] and others applied Gaussian quadrature to evaluate integrals in panel logit and probit models with normal random effects, whereas Waldman [1985] used this routine for the estimation of duration models. Kiefer [1983] developed a series expansion to the same type of integral arising in labour market duration models. Buttler and Moffit [1982] reduced a multivariate normal integral into a univariate one for the panel probit model. Schall [1991] designed an algorithm for the estimation of generalised linear models with random effects obeying relatively weak assumptions. To date though, there does not seem to exist any analytical solution for the maximum likelihood estimator in the framework of a general nonlinear panel data model with random effects having a general parametric distribution. (About an available numerical solutions see, for example, Chapter 23.)
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© 1996 Kluwer Academic Publishers
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Lieberman, O., Mátyás, L. (1996). Improved Estimation Procedures. In: Mátyás, L., Sevestre, P. (eds) The Econometrics of Panel Data. Advanced Studies in Theoretical and Applied Econometrics, vol 33. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0137-7_21
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DOI: https://doi.org/10.1007/978-94-009-0137-7_21
Publisher Name: Springer, Dordrecht
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