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An efficient sequential learning algorithm in regime-switching environments

Author

Listed:
  • Kim Jaeho
  • Lee Sunhyung

    (University of Oklahoma, Department of Economics, 308 Cate Center Drive, Room 158, CCD1,Norman, United States of America)

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.

Suggested Citation

  • Kim Jaeho & Lee Sunhyung, 2019. "An efficient sequential learning algorithm in regime-switching environments," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(3), pages 1-13, June.
    • Handle: RePEc:bpj:sndecm:v:23:y:2019:i:3:p:13:n:4
    DOI: 10.1515/snde-2018-0016
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