Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models
AbstractWe introduce a new efficient importance sampler for nonlinear non-Gaussian state space models. We propose a general and efficient likelihood evaluation method for this class of models via the combination of numerical and Monte Carlo integration methods. Our methodology explores the idea that only a small part of the likelihood evaluation problem requires simulation. We refer to our new method as numerically accelerated importance sampling. The method is computationally and numerically efficient, facilitates parameter estimation for models with high-dimensional state vectors, and overcomes a bias-variance trade-off encountered by other sampling methods. An elaborate simulation study and an empirical application for U.S. stock returns reveal large efficiency gains for a range of models used in financial econometrics.
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Bibliographic InfoPaper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 11-057/4.
Date of creation: 22 Mar 2011
Date of revision: 27 Jan 2012
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State space models; importance sampling; simulated maximum likelihood; stochastic volatility; stochastic copula; stochastic conditional duration;
Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-04-02 (All new papers)
- NEP-CMP-2011-04-02 (Computational Economics)
- NEP-ECM-2011-04-02 (Econometrics)
- NEP-ETS-2011-04-02 (Econometric Time Series)
- NEP-ORE-2011-04-02 (Operations Research)
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- Siem Jan Koopman & Andre Lucas & Marcel Scharth, 2012. "Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models," Tinbergen Institute Discussion Papers 12-020/4, Tinbergen Institute.
- Siem Jan Koopman & Marcel Scharth, 2011.
"The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures,"
Tinbergen Institute Discussion Papers
11-132/4, Tinbergen Institute.
- Siem Jan Koopman, 2012. "The Analysis of Stochastic Volatility in the Presence of Daily Realized Measures," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 11(1), pages 76-115, December.
- Geert Mesters & Siem Jan Koopman, 2012. "Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time," Tinbergen Institute Discussion Papers 12-009/4, Tinbergen Institute.
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