Document Type
Preprint
Publication Date
1-1-2019
Journal / Book Title
Studies in Nonlinear Dynamics and Econometrics
Abstract
We provide a novel approach of estimating a regime-switching nonlinear and non-Gaussian state-space model based on a particle learning scheme. In particular, we extend the particle learning method in Liu, J., and M. West. 2001. "Combined Parameter and State Estimation in Simulation-Based Filtering." In Sequential Monte Carlo Methods in Practice, 197-223. Springer. 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 empirical illustration, the proposed algorithm is used to analyze the underlying dynamics of US excess stock return.
DOI
10.1515/snde-2018-0016
MSU Digital Commons Citation
Kim, Jaeho and Lee, Sunhyung, "An efficient sequential learning algorithm in regime-switching environments" (2019). Department of Economics Faculty Scholarship and Creative Works. 78.
https://digitalcommons.montclair.edu/economics-facpubs/78
Published Citation
Kim, Jaeho and Lee, Sunhyung, An Efficient Sequential Learning Algorithm in Regime-Switching Environments (January 31, 2018). Available at SSRN: https://ssrn.com/abstract=3119222 or http://dx.doi.org/10.2139/ssrn.3119222