An efficient sequential learning algorithm in regime-switching environments

Jaeho Kim, Sunhyung Lee

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number20180016
JournalStudies in Nonlinear Dynamics and Econometrics
Issue number3
StatePublished - 2019


  • parameter learning
  • particle filters
  • regime switching models
  • sequential Monte Carlo estimation
  • volatility models


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