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Non-linear DSGE models and the optimized central difference particle filter

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  • Andreasen, Martin M.

Abstract

We improve the accuracy and speed of particle filtering for non-linear DSGE models with potentially non-normal shocks. This is done by introducing a new proposal distribution which (i) incorporates information from new observables and (ii) has a small optimization step that minimizes the distance to the optimal proposal distribution. A particle filter with this proposal distribution is shown to deliver a high level of accuracy even with relatively few particles, and it is therefore much more efficient than the standard particle filter.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:dyncon:v:35:y:2011:i:10:p:1671-1695
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
    3. Mutschler, Willi, 2015. "Identification of DSGE models—The effect of higher-order approximation and pruning," Journal of Economic Dynamics and Control, Elsevier, vol. 56(C), pages 34-54.
    4. repec:kap:compec:v:51:y:2018:i:3:d:10.1007_s10614-016-9628-6 is not listed on IDEAS
    5. Yang, Yuan & Wang, Lu, 2016. "An auxiliary particle filter for nonlinear dynamic equilibrium models," Economics Letters, Elsevier, vol. 144(C), pages 112-114.

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