Pseudo-panel Data Analysis

Pseudo-Panel Data Analysis is a longitudinal data analysis technique that constructs artificial cohorts from repeated cross-sectional surveys to simulate individual-level panel data, thereby enabling the examination of dynamic behaviors and causal inferences over time without requiring actual panel datasets.

73

Publications

3K

Citations

164

Authors

115

Institutions

Publications per year

2017–2026

23

Variants

Synthetic Panel Data Analysis

Authors

164

  1. DM
    David McKenzie

    World Bank Group, United States

    3 publications · 126 citations

    3

    h-index

  2. SS
    Siqi Song

    National University of Singapore, Singapore

    2 publications · 29 citations

    2

    h-index

  3. LF
    Laura Feiveson

    Federal Reserve Board of Governors, United States

    2 publications · 87 citations

    2

    h-index

  4. RM
    Robert McNown

    University of Colorado Boulder, United States

    2 publications · 101 citations

    2

    h-index

  5. FG
    Francis Green

    University College London, United Kingdom

    2 publications · 84 citations

    2

    h-index

1–5 of 164

Venues

  1. SSRN Electronic Journal

    Repository · 4 publications · 101 citations

    4

    h-index

  2. European Journal of Operational Research

    2 publications · 76 citations

    2

    h-index

  3. Finance and Economics Discussion Series

    FEDS · 2 publications · 87 citations

    2

    h-index

  4. Econstor (Econstor)

    Repository · 2 publications · 24 citations

    2

    h-index

  5. Journal of Business and Economic Statistics

    JBES · 2 publications · 80 citations

    2

    h-index

1–5