A Flexible State-Space Model with Application to Stochastic Volatility
Mardi | 2017-10-10 Salle des thèses 16h – 17h20 Yang LU – Christian GOURIEROUX We introduce a general state-space (or latent factor) model for time series and panel data. The state process has a polynomial expansion based dynamics that can approximate any Markov dynamics arbitrarily well, and has a latent, endogenous switching regime interpretation. The resulting state-space model is associated with simulation-free, recursive formulas for prediction and filtering, as well as the maximum composite likelihood estimation method, which has an […]