An efficient sequential learning algorithm in regime-switching environments

Kim Jaeho () and Lee Sunhyung
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Kim Jaeho: University of Oklahoma, Department of Economics, 308 Cate Center Drive, Room 158, CCD1,Norman, United States of America
Lee Sunhyung: University of Oklahoma, Department of Economics, 308 Cate Center Drive, Room 158, CCD1,Norman, United States of America

Studies in Nonlinear Dynamics & Econometrics, 2019, vol. 23, issue 3, 13

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.

Keywords: regime switching models; sequential Monte Carlo estimation; particle filters; parameter learning; volatility models (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/snde-2018-0016

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