Fast Efficient Importance Sampling by State Space Methods
AbstractWe 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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Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 12-008/4.
Date of creation: 12 Jan 2012
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Kalman filter; Monte Carlo maximum likelihood; Simulation smoothing;
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- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-08-23 (All new papers)
- NEP-ECM-2012-08-23 (Econometrics)
- NEP-ETS-2012-08-23 (Econometric Time Series)
- NEP-ORE-2012-08-23 (Operations Research)
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