An Efficient Filtering Approach to Likelihood Approximation for State-Space Representations
AbstractWe 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. --
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Bibliographic InfoPaper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics Working Papers with number 2007,25.
Date of creation: 2007
Date of revision:
particle filter; adaption; efficient importance sampling; kernel density approximation;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-11-03 (All new papers)
- NEP-ECM-2007-11-03 (Econometrics)
- NEP-ETS-2007-11-03 (Econometric Time Series)
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- Jesús Fernández-Villaverde & Juan F Rubio-Ramírez, 2007.
"How Structural Are Structural Parameters?,"
843644000000000057, UCLA Department of Economics.
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