Dynamic Count Models

Dynamic Count Models is a class of statistical models for count data that incorporate temporal dependence by allowing the current count outcome to depend on past observations or latent states, thereby extending static count models through the inclusion of dynamic components.

32

Publications

4K

Citations

82

Authors

53

Institutions

Publications per year

2017–2026

5

Variants

Dynamic Count Data Models

Authors

82

  1. RB
    Richard Blundell

    University College London, United Kingdom

    3 publications · 2.7K citations

    3

    h-index

  2. RG
    Rachel Griffith

    Institute for Fiscal Studies, United Kingdom

    3 publications · 2.7K citations

    3

    h-index

  3. BL
    Bradford L. Barham

    University of Wisconsin–Madison, United States

    2 publications · 62 citations

    2

    h-index

  4. KK
    Kwansoo Kim

    Korea Advanced Institute of Science and Technology, South Korea

    2 publications · 62 citations

    2

    h-index

  5. AA
    Ayan Acharya

    The University of Texas at Austin, United States

    2 publications · 45 citations

    2

    h-index

1–5 of 82

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 39 citations

    2

    h-index

  2. Journal of Econometrics

    2 publications · 845 citations

    2

    h-index

  3. RePEc: Research Papers in Economics

    Repository · 1 publications · 68 citations

    1

    h-index

  4. The Economic Journal

    1 publications · 541 citations

    1

    h-index

  5. Journal of Economic Dynamics and Control

    1 publications · 66 citations

    1

    h-index

1–5