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Koopman, Siem Jan
Lucas, Andre
Scharth, Marcel
Year of Publication: 
Series/Report no.: 
Tinbergen Institute Discussion Paper No. 11-057/4
We 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.
State space models
importance sampling
simulated maximum likelihood
stochastic volatility
stochastic copula
stochastic conditional duration
Document Type: 
Working Paper

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