IDEAS home Printed from https://ideas.repec.org/p/cte/wsrepe/6266.html

Asymptotic properties for a simulated pseudo maximum likelihood estimator

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/rest/api/core/bitstreams/ecf58998-390f-4604-8668-88c5bb698b64/content
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. repec:adr:anecst:y:1991:i:20-21:p:04 is not listed on IDEAS
    3. Ramos, Rogelio Q. & Pantula, Sastry G., 1995. "Estimation of nonlinear random coefficient models," Statistics & Probability Letters, Elsevier, vol. 24(1), pages 49-56, July.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Concordet, Didier & Nunez, Olivier G., 2002. "A simulated pseudo-maximum likelihood estimator for nonlinear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 39(2), pages 187-201, April.
    2. Benedikt M. Potscher & Ingmar R. Prucha, 1994. "On the Formulation of Uniform Laws of Large Numbers: A Truncation Approach," NBER Technical Working Papers 0085, National Bureau of Economic Research, Inc.
    3. Freyberger, Joachim, 2015. "Asymptotic theory for differentiated products demand models with many markets," Journal of Econometrics, Elsevier, vol. 185(1), pages 162-181.
    4. Iglesias, Emma M. & Linton, Oliver, 2009. "Estimation of tail thickness parameters from GJR-GARCH models," UC3M Working papers. Economics we094726, Universidad Carlos III de Madrid. Departamento de Economía.
    5. Denis Bolduc & Mustapha Kaci, 1993. "Estimation des modèles probit polytomiques : un survol des techniques," L'Actualité Economique, Société Canadienne de Science Economique, vol. 69(3), pages 161-191.
    6. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 207-225.
    7. Ryoko Ito, 2016. "Asymptotic Theory for Beta-t-GARCH," Cambridge Working Papers in Economics 1607, Faculty of Economics, University of Cambridge.
    8. Dahl, Christian M. & Levine, Michael, 2006. "Nonparametric estimation of volatility models with serially dependent innovations," Statistics & Probability Letters, Elsevier, vol. 76(18), pages 2007-2016, December.
    9. Patton, Andrew J. & Ziegel, Johanna F. & Chen, Rui, 2019. "Dynamic semiparametric models for expected shortfall (and Value-at-Risk)," Journal of Econometrics, Elsevier, vol. 211(2), pages 388-413.
    10. Jin Seo Cho & Peter C. B. Phillips & Juwon Seo, 2019. "Parametric Inference on the Mean of Functional Data Applied to Lifetime Income Curves," Working papers 2019rwp-153, Yonsei University, Yonsei Economics Research Institute.
    11. van Garderen, Kees Jan & Lee, Kevin & Pesaran, M. Hashem, 2000. "Cross-sectional aggregation of non-linear models," Journal of Econometrics, Elsevier, vol. 95(2), pages 285-331, April.
    12. Rosa L. Matzkin, 1989. "A Nonparametric Maximum Rank Correlation Estimator," Cowles Foundation Discussion Papers 918, Cowles Foundation for Research in Economics, Yale University.
    13. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2010. "Approximating the critical values of Cramér-von Mises tests in general parametric conditional specifications," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 625-636, March.
    14. Hwan Seo, Myung, 2011. "Estimation Of Nonlinear Error Correction Models," Econometric Theory, Cambridge University Press, vol. 27(2), pages 201-234, April.
    15. Christine Amsler & Artem Prokhorov & Peter Schmidt, 2014. "Using Copulas to Model Time Dependence in Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 497-522, August.
    16. Mayer, Walter J., 1999. "An extension of the maximum score estimator for disequilibrium models," Economics Letters, Elsevier, vol. 64(2), pages 143-149, August.
    17. Marcelo Moreira & Geert Ridder, 2019. "Efficiency loss of asymptotically efficient tests in an instrumental variables regression," CeMMAP working papers CWP03/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Gregory Connor & Matthias Hagmann & Oliver Linton, 2007. "Efficient Estimation of a Semiparametric Characteristic- Based Factor Model of Security Returns," Swiss Finance Institute Research Paper Series 07-26, Swiss Finance Institute.
    19. Emmanuel Duguet & Claire Lelarge, 2012. "Does Patenting Increase the Private Incentives to Innovate? A Microeconometric Analysis," Annals of Economics and Statistics, GENES, issue 107-108, pages 201-238.
    20. de Jong, Robert M. & Woutersen, Tiemen, 2011. "Dynamic Time Series Binary Choice," Econometric Theory, Cambridge University Press, vol. 27(4), pages 673-702, August.

    More about this item

    Keywords

    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cte:wsrepe:6266. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ana Poveda (email available below). General contact details of provider: http://portal.uc3m.es/portal/page/portal/dpto_estadistica .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.