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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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  • 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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    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. 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.
    6. 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.
    7. McFadden, Daniel & Ruud, Paul A, 1994. "Estimation by Simulation," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 591-608, November.
    8. Steven Stern, 1997. "Simulation-Based Estimation," Journal of Economic Literature, American Economic Association, vol. 35(4), pages 2006-2039, December.
    9. Jean-Francois Richard, 2007. "Efficient High-Dimensional Importance Sampling," Working Paper 321, Department of Economics, University of Pittsburgh, revised Jan 2007.
    10. Keane, Michael, 1993. "Simulation estimation for panel data models with limited dependent variables," MPRA Paper 53029, University Library of Munich, Germany.
    11. 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. Ruggero Bellio & Nicola Soriani, 2021. "Maximum likelihood estimation based on the Laplace approximation for p2 network regression models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(1), pages 24-41, February.
    3. 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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