Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels

Natalia Khorunzhina and Jean-Francois Richard

Computational Economics, 2019, vol. 53, issue 3, No 5, 1017 pages

Abstract: Abstract The objective of the paper is that of constructing finite Gaussian mixture approximations to analytically intractable density kernels. The proposed method is adaptive in that terms are added one at the time and the mixture is fully re-optimized at each step using a distance measure that approximates the corresponding importance sampling variance. All functions of interest are evaluated under Gaussian product rules. Since product rules suffer from an obvious curse of dimensionality, the proposed algorithm as presented is only applicable to models whose non-linear and/or non-Gaussian subspace is of dimension up to three. Extensions to higher-dimensional applications would require the use of sparse grids, as discussed in the paper. Examples include a sequential (filtering) evaluation of the likelihood function of a stochastic volatility model where all relevant densities (filtering, predictive and likelihood) are closely approximated by mixtures.

Keywords: Finite mixture; Distance measure; Gaussian quadrature; Importance sampling; Adaptive algorithm; Stochastic volatility; Density kernel (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-017-9777-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: Finite Gaussian Mixture Approximations to Analytically Intractable Density Kerkels (2016)
Working Paper: Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels (2016)
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:kap:compec:v:53:y:2019:i:3:d:10.1007_s10614-017-9777-2

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-017-9777-2

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().