Bayesian Autoregressive Models

Bayesian Autoregressive Models is a statistical modeling framework that belongs to the class of time series models in which future values are predicted as a linear combination of past observations, distinguished by the incorporation of Bayesian inference to estimate model parameters and quantify uncertainty through posterior distributions.

234

Publications

14.5K

Citations

450

Authors

276

Institutions

Publications per year

2017–2026

61

Authors

450

  1. RG
    Rangan Gupta

    University of Pretoria, South Africa

    9 publications · 280 citations

    9

    h-index

  2. PÖ
    Pär Österholm

    National Institute of Economic Research, Sweden

    8 publications · 161 citations

    8

    h-index

  3. SM
    Stephen M. Miller

    University of Nevada, Las Vegas, United States

    5 publications · 111 citations

    5

    h-index

  4. DK
    Dimitris Korobilis

    University of Glasgow, United Kingdom

    4 publications · 288 citations

    4

    h-index

  5. RK
    Robert Kohn

    UNSW Sydney, Australia

    4 publications · 170 citations

    4

    h-index

1–5 of 450

Venues

  1. Journal of Econometrics

    15 publications · 982 citations

    14

    h-index

  2. SSRN Electronic Journal

    Repository · 16 publications · 770 citations

    13

    h-index

  3. Journal of Business and Economic Statistics

    JBES · 12 publications · 2.9K citations

    12

    h-index

  4. International Journal of Forecasting

    11 publications · 685 citations

    11

    h-index

  5. Journal of Time Series Analysis

    7 publications · 363 citations

    7

    h-index

1–5