Bayesian Regime Switching Models

Bayesian Regime Switching Models is a statistical framework within time series analysis that combines state-space modeling with Bayesian inference to capture structural changes in dynamics over time, where discrete latent regimes govern parameter shifts according to a Markov process.

34

Publications

1.5K

Citations

74

Authors

65

Institutions

Publications per year

2017–2026

7

Variants

Bayesian Markov-switching Models

Authors

74

  1. AR
    Alex R. Cook

    National University of Singapore, Singapore

    2 publications · 35 citations

    2

    h-index

  2. TD
    Taeyoung Doh

    Federal Reserve Bank of Kansas City, United States

    2 publications · 132 citations

    2

    h-index

  3. JT
    Jue Tao Lim

    National University of Singapore, Singapore

    2 publications · 35 citations

    2

    h-index

  4. JT
    Jun Tu

    Singapore Management University, Singapore

    2 publications · 132 citations

    2

    h-index

  5. PE
    Pedro Eduardo Ferreira

    Karolinska Institutet, Sweden

    2 publications · 146 citations

    2

    h-index

1–5 of 74

Venues

  1. SSRN Electronic Journal

    Repository · 6 publications · 124 citations

    6

    h-index

  2. 2

    h-index

  3. Journal of Applied Econometrics

    2 publications · 68 citations

    2

    h-index

  4. Studies in Nonlinear Dynamics and Econometrics

    SNDE · 1 publications · 36 citations

    1

    h-index

  5. Water Resources Research

    1 publications · 51 citations

    1

    h-index

1–5