Heteroscedasticity Modeling

Heteroscedasticity Modeling is a statistical modeling approach that explicitly accounts for non-constant variance in error terms across observations, distinguishing itself from homoscedastic models by incorporating systematic changes in dispersion relative to predictor variables or time.

1.1K

Publications

102.1K

Citations

1.9K

Authors

879

Institutions

Publications per year

2017–2026

237

Authors

1.9K

  1. FC
    Francisco Cribari‐Neto

    Universidade Federal de Pernambuco, Brazil

    12 publications · 782 citations

    12

    h-index

  2. RJ
    Raymond J. Carroll

    Texas A&M University, United States

    12 publications · 2.5K citations

    12

    h-index

  3. RR
    Rand R. Wilcox

    University of Southern California, United States

    13 publications · 272 citations

    11

    h-index

  4. DR
    David Ruppert

    Cornell University, United States

    8 publications · 1.5K citations

    8

    h-index

  5. LG
    L. G. Godfrey

    University of York, United Kingdom

    8 publications · 530 citations

    8

    h-index

1–5 of 1.9K

Venues

  1. Journal of the American Statistical Association

    52 publications · 5.9K citations

    34

    h-index

  2. Journal of Econometrics

    49 publications · 4.9K citations

    30

    h-index

  3. Journal of Business and Economic Statistics

    JBES · 41 publications · 5.2K citations

    28

    h-index

  4. Econometrica

    20 publications · 36.6K citations

    20

    h-index

  5. SSRN Electronic Journal

    Repository · 32 publications · 937 citations

    17

    h-index

1–5