Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/322137 
Authors: 
Year of Publication: 
2025
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2025-034/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
This paper introduces a novel approach to simulation smoothing for nonlinear and non-Gaussian state space models. It allows for computing smoothed estimates of the states and nonlinear functions of the states, as well as visualizing the joint smoothing distribution. The approach combines extremum estimation with simulated data from the model to estimate the conditional distributions in the backward smoothing decomposition. The method is generally applicable and can be paired with various estimators of conditional distributions. Several applications to nonlinear models are presented for illustration. An empirical application based on a stochastic volatility model with stable errors highlights the flexibility of the approach.
Subjects: 
Amortized inference
Fixed-interval smoothing
Importance sampling
Latent variables
Stable distribution
Stochastic volatility
Document Type: 
Working Paper

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