A1517
Title: Semi-parametric Bayesian forecasting with an application to stochastic volatility
Authors: Martina Danielova Zaharieva - Erasmus University Rotterdam (Netherlands) [presenting]
Fabian Goessling - University of Muenster (Germany)
Abstract: A new and highly flexible Bayesian sampling algorithm is proposed for non-linear state space models under non-parametric distributions. The estimation framework combines a particle filtering and smoothing algorithm for the latent process with a Dirichlet process mixture model for the error term of the observable variables. In particular, we overcome the problem of constraining the models by transformations or the need for conjugate distributions. We use the Chinese restaurant representation of the Dirichlet process mixture, which allows for a parsimonious and generally applicable sampling algorithm. Thus, our estimation algorithm combines a pseudo marginal Metropolis Hastings scheme with a marginalized hierarchical semi-parametric model. We test our approach for several nested model specifications using simulated data and provide density forecasts. Furthermore, we carry out a real data example.
