Cross-sectional Aggregation

Cross-Sectional Aggregation is a methodological approach in aggregate demand forecasting that combines data from multiple entities or categories at a single point in time to improve forecast accuracy by leveraging interunit relationships and patterns across the cross-section.

30

Publications

3.3K

Citations

53

Authors

44

Institutions

Publications per year

2017–2026

5

Authors

53

  1. TM
    Thomas M. Stoker

    Massachusetts Institute of Technology, United States

    3 publications · 91 citations

    3

    h-index

  2. PM
    P. Michael Laub

    Federal Reserve Board of Governors, United States

    2 publications · 41 citations

    2

    h-index

  3. MH
    M. Hashem Pesaran

    University of Southern California, United States

    2 publications · 137 citations

    2

    h-index

  4. JE
    J. Eduardo Vera‐Valdés

    Aalborg University, Denmark

    2 publications · 46 citations

    2

    h-index

  5. NV
    Nina van Loon

    Aarhus University, Denmark

    1 publications · 112 citations

    1

    h-index

1–5 of 53

Venues

  1. 5

    h-index

  2. Econometric Reviews

    2 publications · 93 citations

    2

    h-index

  3. SSRN Electronic Journal

    Repository · 2 publications · 24 citations

    2

    h-index

  4. Journal of Econometrics

    2 publications · 107 citations

    2

    h-index

  5. International Economic Review

    1 publications · 31 citations

    1

    h-index

1–5