Arbeitspapier
Fast Efficient Importance Sampling by State Space Methods
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.
- Language
-
Englisch
- Bibliographic citation
-
Series: Tinbergen Institute Discussion Paper ; No. 12-008/4
- Classification
-
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Construction and Estimation
- Subject
-
Kalman filter
Monte Carlo maximum likelihood
Simulation smoothing
Zustandsraummodell
Maximum-Likelihood-Methode
Monte-Carlo-Methode
Simulation
Theorie
- Event
-
Geistige Schöpfung
- (who)
-
Koopman, Siem Jan
Nguyen, Thuy Minh
- Event
-
Veröffentlichung
- (who)
-
Tinbergen Institute
- (where)
-
Amsterdam and Rotterdam
- (when)
-
2012
- Handle
- Last update
- 10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Koopman, Siem Jan
- Nguyen, Thuy Minh
- Tinbergen Institute
Time of origin
- 2012
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