Bayesian Time Series Analysis

Bayesian Time Series Analysis is a statistical methodology that belongs to the class of Bayesian inference techniques, distinguished by its application to temporally ordered data for modeling and forecasting dynamic processes through the incorporation of prior knowledge and sequential updating of posterior distributions.

1.3K

Publications

75.7K

Citations

2.8K

Authors

1.3K

Institutions

Publications per year

2017–2026

425

Authors

2.8K

  1. GK
    Gary Koop

    University of Strathclyde, United Kingdom

    24 publications · 1.6K citations

    19

    h-index

  2. SP
    Simon Potter

    Federal Reserve Bank of New York, United States

    12 publications · 723 citations

    12

    h-index

  3. HK
    Herman K. van Dijk

    Erasmus University Rotterdam, Netherlands

    9 publications · 524 citations

    9

    h-index

  4. MW
    Mike West

    Duke University, United States

    9 publications · 1K citations

    9

    h-index

  5. AC
    Andrea Carriero

    Bocconi University, Italy

    9 publications · 885 citations

    9

    h-index

1–5 of 2.8K

Venues

  1. Journal of the American Statistical Association

    41 publications · 3.5K citations

    28

    h-index

  2. SSRN Electronic Journal

    Repository · 50 publications · 2.3K citations

    23

    h-index

  3. Journal of Business and Economic Statistics

    JBES · 26 publications · 2.8K citations

    21

    h-index

  4. Journal of Applied Econometrics

    24 publications · 3.6K citations

    20

    h-index

  5. Journal of Econometrics

    24 publications · 1.5K citations

    19

    h-index

1–5