An Efficient Sequential Learning Algorithm in Regime-Switching Environments

Abstract

We provide a noble approach of estimating a regime-switching nonlinear and non-Gaussian state space model based on a particle learning scheme. Particularly, we extend the particle learning method in Liu and West (2001) by constructing a new proposal distribution for the latent regime index variable that incorporates all available information contained in the current and past observations. The Monte Carlo simulation result implies that our approach categorically outperforms a popular existing algorithm. For an empirical illustration, the proposed algorithm is used to analyze the underlying dynamics of the U.S. excess stock return.