Misspecification Robustness

Misspecification Robustness is a property of an estimation method that ensures consistent and asymptotically valid inference even when the assumed model does not perfectly capture the true data-generating process.

162

Publications

10.7K

Citations

321

Authors

227

Institutions

Publications per year

2017–2026

29

Authors

321

  1. AK
    Anil K. Bera

    University of Illinois Urbana-Champaign, United States

    4 publications · 300 citations

    4

    h-index

  2. GM
    Gabriel Montes‐Rojas

    City, University of London, United Kingdom

    3 publications · 49 citations

    3

    h-index

  3. MM
    Marialuz Moreno Badia

    International Monetary Fund, United States

    3 publications · 56 citations

    3

    h-index

  4. TJ
    Thomas J. Sargent

    New York University, United States

    3 publications · 428 citations

    3

    h-index

  5. RK
    Raymond Kan

    University of Toronto, Canada

    3 publications · 496 citations

    3

    h-index

1–5 of 321

Venues

  1. Journal of Econometrics

    11 publications · 622 citations

    11

    h-index

  2. SSRN Electronic Journal

    Repository · 10 publications · 191 citations

    10

    h-index

  3. Canadian Journal of Statistics

    CJS · 5 publications · 121 citations

    5

    h-index

  4. Economics Letters

    5 publications · 73 citations

    5

    h-index

  5. Econometrica

    4 publications · 162 citations

    4

    h-index

1–5