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Asymptotic properties for a simulated pseudo maximum likelihood estimator

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  • Núñez, Olivier

Abstract

We propose an estimator for parameters of nonlinear mixed effects model, obtained by maximization of a simulated pseudo likelihood. This simulated criterion is constructed from the likelihood of a Gaussian model whose means and variances are given by Monte Carlo approximations of means and variances of the true model. If the number of experimental units and the sample size of Monte Carlo simulations are respectively denoted by N and K, we obtained the strong consistency and asymptotic normality of the estimator when the ratio NJ/2 /K tends to zero.

Suggested Citation

  • Núñez, Olivier, 1998. "Asymptotic properties for a simulated pseudo maximum likelihood estimator," DES - Working Papers. Statistics and Econometrics. WS 6266, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:6266
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    References listed on IDEAS

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    1. Christian Gouriéroux & Alain Monfort, 1991. "Simulation Based Inference in Models with Heterogeneity," Annals of Economics and Statistics, GENES, issue 20-21, pages 69-107.
    2. Ramos, Rogelio Q. & Pantula, Sastry G., 1995. "Estimation of nonlinear random coefficient models," Statistics & Probability Letters, Elsevier, vol. 24(1), pages 49-56, July.
    3. Andrews, Donald W K, 1987. "Consistency in Nonlinear Econometric Models: A Generic Uniform Law of Large Numbers [On Unification of the Asymptotic Theory of Nonlinear Econometric Models]," Econometrica, Econometric Society, vol. 55(6), pages 1465-1471, November.
    4. repec:adr:anecst:y:1991:i:20-21:p:04 is not listed on IDEAS
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