Conditional Density Forecasting

Conditional Density Forecasting is a probabilistic forecasting framework that models the full conditional probability distribution of a future outcome given a set of explanatory variables and historical information, thereby enabling comprehensive uncertainty quantification in predictive analytics.

57

Publications

3.2K

Citations

125

Authors

87

Institutions

Publications per year

2017–2026

13

Authors

125

  1. AS
    Anthony S. Tay

    Singapore Management University, Singapore

    4 publications · 707 citations

    4

    h-index

  2. FX
    Francis X. Diebold

    University of Pennsylvania, United States

    4 publications · 458 citations

    4

    h-index

  3. KF
    Kenneth F. Wallis

    University of Warwick, United Kingdom

    3 publications · 350 citations

    3

    h-index

  4. JW
    James W. Taylor

    University of Wisconsin–Madison, United States

    3 publications · 403 citations

    3

    h-index

  5. PA
    Piergiorgio Alessandri

    Bank of Italy, Italy

    2 publications · 197 citations

    2

    h-index

1–5 of 125

Venues

  1. Journal of Forecasting

    9 publications · 863 citations

    9

    h-index

  2. SSRN Electronic Journal

    Repository · 7 publications · 196 citations

    7

    h-index

  3. International Journal of Forecasting

    5 publications · 228 citations

    5

    h-index

  4. IEEE Transactions on Smart Grid

    3 publications · 240 citations

    3

    h-index

  5. RePEc: Research Papers in Economics

    Repository · 3 publications · 102 citations

    3

    h-index

1–5