Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models

Siem Jan Koopman, Andre Lucas and Marcel Scharth ()
Additional contact information
Marcel Scharth: VU University Amsterdam

No 11-057/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This paper led to a publication in the 'Journal of Business & Economic Statistics' , 2015, 33 (1), 114-127.

We introduce a new efficient importance sampler for nonlinear non-Gaussian state space models. We propose a general and efficient likelihood evaluation method for this class of models via the combination of numerical and Monte Carlo integration methods. Our methodology explores the idea that only a small part of the likelihood evaluation problem requires simulation. We refer to our new method as numerically accelerated importance sampling. The method is computationally and numerically efficient, facilitates parameter estimation for models with high-dimensional state vectors, and overcomes a bias-variance trade-off encountered by other sampling methods. An elaborate simulation study and an empirical application for U.S. stock returns reveal large efficiency gains for a range of models used in financial econometrics.

Keywords: State space models; importance sampling; simulated maximum likelihood; stochastic volatility; stochastic copula; stochastic conditional duration (search for similar items in EconPapers)
JEL-codes: C15 C22 (search for similar items in EconPapers)
Date: 2011-03-22, Revised 2012-01-27
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://papers.tinbergen.nl/11057.pdf (application/pdf)

Related works:
Journal Article: Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models (2015)
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:20110057

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 ().