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Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels

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  • Khorunzhina, Natalia
  • Richard, Jean-Francois

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 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.

Suggested Citation

  • Khorunzhina, Natalia & Richard, Jean-Francois, 2016. "Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels," MPRA Paper 72326, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:72326
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    More about this item

    Keywords

    Finite mixture; Distance measure; Gaussian quadrature; Importance sampling; Adaptive algorithm; Stochastic volatility; Density kernel;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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