Forecasting Experiments

Forecasting Experiments is a systematic research methodology within the domain of learning in experiments that involves the design and execution of controlled empirical studies to evaluate and improve the accuracy, reliability, and generalizability of predictive models through iterative hypothesis testing and data-driven validation.

110

Publications

6.3K

Citations

283

Authors

186

Institutions

Publications per year

2017–2026

37

Authors

283

  1. MG
    Massimo Guidolin

    Bocconi University, Italy

    7 publications · 724 citations

    7

    h-index

  2. AT
    Allan Timmermann

    University of California San Diego, United States

    7 publications · 724 citations

    7

    h-index

  3. CH
    Cars Hommes

    University of Amsterdam, Netherlands

    6 publications · 272 citations

    6

    h-index

  4. NH
    Nigel Harvey

    University College London, United Kingdom

    4 publications · 201 citations

    4

    h-index

  5. AL
    Augustin Landier

    HEC Paris, France

    4 publications · 256 citations

    4

    h-index

1–5 of 283

Venues

  1. SSRN Electronic Journal

    Repository · 12 publications · 485 citations

    12

    h-index

  2. Journal of Forecasting

    4 publications · 119 citations

    4

    h-index

  3. International Journal of Forecasting

    4 publications · 141 citations

    4

    h-index

  4. Bulletin of the Seismological Society of America

    BSSA · 4 publications · 287 citations

    4

    h-index

  5. Management Science

    3 publications · 201 citations

    3

    h-index

1–5