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Efficient Likelihood Evaluation of State-Space Representations

Author

Listed:
  • David N. DeJong
  • Hariharan Dharmarajan
  • Roman Liesenfeld
  • Guilherme Moura
  • Jean-Francois Richard

Abstract

We develop a numerical procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-Gaussian state-space models. The procedure employs continuous approximations of filtering densities, and delivers unconditionally optimal global approximations of targeted integrands to achieve likelihood approximation. Optimized approximations of targeted integrands are constructed via efficient importance sampling. Resulting likelihood approximations are continuous functions of model parameters, greatly enhancing parameter estimation. We illustrate our procedure in applications to dynamic stochastic general equilibrium models.

Suggested Citation

  • David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Guilherme Moura & Jean-Francois Richard, 2009. "Efficient Likelihood Evaluation of State-Space Representations," Working Papers 2009/15, Czech National Bank.
    • Handle: RePEc:cnb:wpaper:2009/15
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    References listed on IDEAS

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    8. David N. DeJong & Hariharan Dharmarajan & Liesenfeld Roman & Richard Jean-Francois, 2007. "Efficient Filtering in State-Space Representations," Working Paper 317, Department of Economics, University of Pittsburgh, revised Aug 2009.
    9. Pitt, Michael K., 2002. "Smooth particle filters for likelihood evaluation and maximisation," Economic Research Papers 269464, University of Warwick - Department of Economics.
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    14. Jean-Francois Richard, 2007. "Efficient High-Dimensional Importance Sampling," Working Paper 321, Department of Economics, University of Pittsburgh, revised Jan 2007.
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    Citations

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    Cited by:

    1. Jean-François Richard, 2015. "Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables," Working Paper 5778, Department of Economics, University of Pittsburgh.
    2. Steffen R. Henzel & Malte Rengel, 2017. "Dimensions Of Macroeconomic Uncertainty: A Common Factor Analysis," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 843-877, April.
    3. Yang, Yuan & Wang, Lu, 2015. "An Improved Auxiliary Particle Filter for Nonlinear Dynamic Equilibrium Models," Dynare Working Papers 47, CEPREMAP.
    4. Marta Boczoń & Jean-François Richard, 2020. "Balanced Growth Approach to Tracking Recessions," Econometrics, MDPI, vol. 8(2), pages 1-35, April.
    5. Moura, Guilherme V. & Turatti, Douglas Eduardo, 2014. "Efficient estimation of conditionally linear and Gaussian state space models," Economics Letters, Elsevier, vol. 124(3), pages 494-499.
    6. Hall, Jamie & Pitt, Michael K. & Kohn, Robert, 2014. "Bayesian inference for nonlinear structural time series models," Journal of Econometrics, Elsevier, vol. 179(2), pages 99-111.
    7. Andras Fulop & Jeremy Heng & Junye Li, 2022. "Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models," Papers 2201.01094, arXiv.org.
    8. Ozturk, Serda Selin & Richard, Jean-Francois, 2015. "Stochastic volatility and leverage: Application to a panel of S&P500 stocks," Finance Research Letters, Elsevier, vol. 12(C), pages 67-76.
    9. Andreasen, Martin M., 2011. "Non-linear DSGE models and the optimized central difference particle filter," Journal of Economic Dynamics and Control, Elsevier, vol. 35(10), pages 1671-1695, October.
    10. Kleppe, Tore Selland & Oglend, Atle, 2017. "Estimating the competitive storage model: A simulated likelihood approach," Econometrics and Statistics, Elsevier, vol. 4(C), pages 39-56.

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    More about this item

    Keywords

    Adaption; dynamic stochastic general equilibrium model; efficient importance sampling; kernel density approximation; particle filter.;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

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