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Efficient simulated maximum likelihood estimation through explicitly parameter dependent importance sampling

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  • Christian Brinch

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Suggested Citation

  • Christian Brinch, 2012. "Efficient simulated maximum likelihood estimation through explicitly parameter dependent importance sampling," Computational Statistics, Springer, vol. 27(1), pages 13-28, March.
  • Handle: RePEc:spr:compst:v:27:y:2012:i:1:p:13-28
    DOI: 10.1007/s00180-011-0230-z
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    File URL: http://hdl.handle.net/10.1007/s00180-011-0230-z
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    References listed on IDEAS

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    1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. J. Durbin & S. J. Koopman, 2000. "Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 3-56.
    3. Koopman, Siem Jan & Shephard, Neil & Creal, Drew, 2009. "Testing the assumptions behind importance sampling," Journal of Econometrics, Elsevier, vol. 149(1), pages 2-11, April.
    4. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    5. repec:pit:wpaper:321 is not listed on IDEAS
    6. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
    7. Keane, Michael, 1993. "Simulation estimation for panel data models with limited dependent variables," MPRA Paper 53029, University Library of Munich, Germany.
    8. Skaug, Hans J. & Fournier, David A., 2006. "Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 699-709, November.
    9. Richard, Jean-Francois & Zhang, Wei, 2007. "Efficient high-dimensional importance sampling," Journal of Econometrics, Elsevier, vol. 141(2), pages 1385-1411, December.
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    Cited by:

    1. Torres, Marcelo de Oliveira & Felthoven, Ronald G., 2012. "Productivity Growth and Product Choice in Fisheries: the Case of the Alaskan Pollock Fishery Revisited," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124851, Agricultural and Applied Economics Association.
    2. Tue Gorgens & Sanghyeok Lee, 2017. "Estimation of dynamic models of recurring events with censored data," ANU Working Papers in Economics and Econometrics 2017-655, Australian National University, College of Business and Economics, School of Economics.

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