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Fast Efficient Importance Sampling by State Space Methods

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Author Info

  • Siem Jan Koopman

    (VU University Amsterdam)

  • Thuy Minh Nguyen

    (Deutsche Bank, London)

Abstract

We show that efficient importance sampling for nonlinear non-Gaussian state space models can be implemented by computationally efficient Kalman filter and smoothing methods. The result provides some new insights but it primarily leads to a simple and fast method for efficient importance sampling. A simulation study and empirical illustration provide some evidence of the computational gains.

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File URL: http://papers.tinbergen.nl/12008.pdf
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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 12-008/4.

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Date of creation: 12 Jan 2012
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Handle: RePEc:dgr:uvatin:20120008

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Web page: http://www.tinbergen.nl

Related research

Keywords: Kalman filter; Monte Carlo maximum likelihood; Simulation smoothing;

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