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An Efficient Filtering Approach to Likelihood Approximation for State-Space Representations

Author

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  • DeJong, David Neil
  • Dharmarajan, Hariharan
  • Liesenfeld, Roman
  • Richard, Jean-François

Abstract

We develop a numerical filtering procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-gaussian state-space models. The procedure approximates necessary integrals using continuous or piecewise-continuous approximations of target densities. Construction is achieved via efficient importance sampling, and approximating densities are adapted to fully incorporate current information.

Suggested Citation

  • DeJong, David Neil & Dharmarajan, Hariharan & Liesenfeld, Roman & Richard, Jean-François, 2007. "An Efficient Filtering Approach to Likelihood Approximation for State-Space Representations," Economics Working Papers 2007-25, Christian-Albrechts-University of Kiel, Department of Economics.
    • Handle: RePEc:zbw:cauewp:6339
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    Cited by:

    1. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2008. "How Structural Are Structural Parameters?," NBER Chapters, in: NBER Macroeconomics Annual 2007, Volume 22, pages 83-137, National Bureau of Economic Research, Inc.

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    Keywords

    particle filter; adaption; efficient importance sampling; kernel density approximation;
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