Recursive Forecasting

Recursive Forecasting is a time series prediction methodology within forecasting theory that, as a recursive process, iteratively updates its input using prior forecasts rather than actual observations to generate subsequent predictions over multiple horizons.

71

Publications

5.1K

Citations

149

Authors

98

Institutions

Publications per year

2017–2026

17

Variants

Iterative Forecasting

Authors

149

  1. PC
    Peter C. Young

    Lancaster University, United Kingdom

    5 publications · 270 citations

    5

    h-index

  2. CN
    C.N. Ng

    Lancaster University, United Kingdom

    4 publications · 243 citations

    4

    h-index

  3. AT
    Allan Timmermann

    University of California San Diego, United States

    4 publications · 420 citations

    4

    h-index

  4. SB
    Souhaib Ben Taieb

    Monash University, Australia

    3 publications · 111 citations

    3

    h-index

  5. MG
    Massimo Guidolin

    Bocconi University, Italy

    3 publications · 161 citations

    3

    h-index

1–5 of 149

Venues

  1. Journal of Forecasting

    5 publications · 327 citations

    5

    h-index

  2. SSRN Electronic Journal

    Repository · 3 publications · 64 citations

    3

    h-index

  3. RePEc: Research Papers in Economics

    Repository · 3 publications · 102 citations

    3

    h-index

  4. IEEE Signal Processing Letters

    2 publications · 86 citations

    2

    h-index

  5. Journal of Applied Econometrics

    2 publications · 203 citations

    2

    h-index

1–5