A0255
Title: Efficient variational approximations for state space models
Authors: Ruben Laoiza Maya - Monash University (Australia) [presenting]
Didier Nibbering - Monash University (Australia)
Abstract: Variational Bayes methods are a scalable estimation approach for many complex state space models. However, existing methods exhibit a trade-off between accurate estimation and computational efficiency. A variational approximation that mitigates this trade-off is proposed. This approximation is based on importance densities that have been proposed in the context of efficient importance sampling. By direct conditioning on the observed data, the proposed method produces an accurate approximation to the exact posterior distribution. Because the steps required for its calibration are computationally efficient, the approach is faster than existing variational Bayes methods. The proposed method can be applied to any state-space model that has a closed-form measurement density function and a state transition distribution that belongs to the exponential family of distributions. The method is illustrated in numerical experiments with stochastic volatility models and a macroeconomic empirical application using a high-dimensional state space model.
