Finite Gaussian Mixture Approximations to Analytically Intractable Density Kerkels

Jean-Francois Richard

No 5980, Working Paper from Department of Economics, University of Pittsburgh

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 underGaussian quadrature rules. 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.

Date: 2016-01
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Related works:
Journal Article: Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels (2019)
Working Paper: Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels (2016)
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