Simulation Smoothing for Nonlinear non-Gaussian State Space Models using Machine Learning Methods

Karim Moussa and Siem Jan Koopman
Additional contact information
Siem Jan Koopman: Vrije Universiteit Amsterdam

No 25-034/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This paper develops a new simulation smoothing method for nonlinear and non-Gaussian state space models. It can be used to compute full-sample (smoothed) estimates of latent states, nonlinear functions of the states, and their joint density conditional on the data. The simulation smoother can be adopted as an importance sampler for estimating model parameters via Monte Carlo maximum likelihood. The approach relies on simulated data from the model to estimate the conditional distributions in a backward smoothing step. The method is general and can be combined with various estimators of conditional distributions, which enables the use of general machine learning methods. Two empirical applications, one for the volatility index for crypto-currencies (VCRIX) and one for the daily returns for the stock price of Tesla, highlight the flexibility of the method.

Keywords: Extremum Monte Carlo; Fixed-interval smoothing; Importance sampling; Machine learning; State space models; Stochastic volatility (search for similar items in EconPapers)
JEL-codes: C15 C22 C45 C58 (search for similar items in EconPapers)
Date: 2025-05-16, Revised 2026-03-10
New Economics Papers: this item is included in nep-cmp, nep-dcm, nep-ecm and nep-ets
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://papers.tinbergen.nl/25034.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20250034

Access Statistics for this paper

More papers in Tinbergen Institute Discussion Papers from Tinbergen Institute Contact information at EDIRC.
Bibliographic data for series maintained by Tinbergen Office +31 (0)10-4088900 ().