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Maxwell Dylan Kellogg

Personal Details

First Name:Maxwell
Middle Name:Dylan
Last Name:Kellogg
Suffix:
RePEc Short-ID:pke421
[This author has chosen not to make the email address public]
https://sites.google.com/view/maxkellogg

Affiliation

Økonomisk institutt
Universitetet i Oslo

Oslo, Norway
http://www.oekonomi.uio.no/
RePEc:edi:souiono (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Maxwell Kellogg & Magne Mogstad & Guillaume Pouliot & Alexander Torgovitsky, 2020. "Combining Matching and Synthetic Control to Trade off Biases from Extrapolation and Interpolation," NBER Working Papers 26624, National Bureau of Economic Research, Inc.

Articles

  1. Maxwell Kellogg & Magne Mogstad & Guillaume A. Pouliot & Alexander Torgovitsky, 2021. "Combining Matching and Synthetic Control to Tradeoff Biases From Extrapolation and Interpolation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1804-1816, October.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Maxwell Kellogg & Magne Mogstad & Guillaume Pouliot & Alexander Torgovitsky, 2020. "Combining Matching and Synthetic Control to Trade off Biases from Extrapolation and Interpolation," NBER Working Papers 26624, National Bureau of Economic Research, Inc.

    Cited by:

    1. Billy Ferguson & Brad Ross, 2020. "Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error," Papers 2012.15367, arXiv.org, revised Feb 2021.
    2. Sondermann, David & Lehtimäki, Jonne, 2020. "Baldwin vs. Cecchini revisited: the growth impact of the European Single Market," Working Paper Series 2392, European Central Bank.
    3. Hollingsworth, Alex & Wing, Coady, 2020. "Tactics for design and inference in synthetic control studies: An applied example using high-dimensional data," SocArXiv fc9xt, Center for Open Science.
    4. Callaway, Brantly & Li, Tong, 2023. "Policy evaluation during a pandemic," Journal of Econometrics, Elsevier, vol. 236(1).
    5. Stefano, Roberta di & Mellace, Giovanni, 2020. "The inclusive synthetic control method," Discussion Papers on Economics 14/2020, University of Southern Denmark, Department of Economics.
    6. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
    7. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
    8. Clark, Robert & Fabiilli, Christopher & Lasio, Laura, 2022. "Collusion in the US generic drug industry," International Journal of Industrial Organization, Elsevier, vol. 85(C).
    9. Jonne Lehtimäki & David Sondermann, 2022. "Baldwin versus Cecchini revisited: the growth impact of the European Single Market," Empirical Economics, Springer, vol. 63(2), pages 603-635, August.

Articles

  1. Maxwell Kellogg & Magne Mogstad & Guillaume A. Pouliot & Alexander Torgovitsky, 2021. "Combining Matching and Synthetic Control to Tradeoff Biases From Extrapolation and Interpolation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1804-1816, October.

    Cited by:

    1. Callaway, Brantly & Karami, Sonia, 2023. "Treatment effects in interactive fixed effects models with a small number of time periods," Journal of Econometrics, Elsevier, vol. 233(1), pages 184-208.
    2. Robert Kraemer & Jonne Lehtimäki, 2024. "Government debt, European Institutions and fiscal rules: a synthetic control approach," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 31(4), pages 1112-1157, August.
    3. Omid M. Ardakani & N. Kundan Kishor & Suyong Song, 2024. "Does membership of the EMU matter for economic and financial outcomes?," Contemporary Economic Policy, Western Economic Association International, vol. 42(3), pages 416-447, July.
    4. Stefano, Roberta di & Mellace, Giovanni, 2020. "The inclusive synthetic control method," Discussion Papers on Economics 14/2020, University of Southern Denmark, Department of Economics.
    5. Li, Xingyu & Shen, Yan & Zhou, Qiankun, 2024. "Confidence intervals of treatment effects in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 240(1).
    6. Marco Francesconi & Jonathan James, 2022. "Alcohol Price Floors and Externalities: The Case of Fatal Road Crashes," CESifo Working Paper Series 9745, CESifo.
    7. Bogatyrev, Konstantin & Stoetzer, Lukas, 2024. "Synthetic Control Methods for Proportions," OSF Preprints brhd3, Center for Open Science.
    8. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    9. Guillaume Allaire Pouliot & Zhen Xie, 2022. "Degrees of Freedom and Information Criteria for the Synthetic Control Method," Papers 2207.02943, arXiv.org.
    10. Maria Petrillo & Daniel Valdenegro & Charles Rahal & Yanan Zhang & Gwilym Pryce & Matthew R. Bennett, 2024. "Estimating the Cost of Informal Care with a Novel Two-Stage Approach to Individual Synthetic Control," Papers 2411.10314, arXiv.org, revised Nov 2024.
    11. Zongwu Cai & Ying Fang & Ming Lin & Zixuan Wu, 2023. "A Quasi Synthetic Control Method for Nonlinear Models With High-Dimensional Covariates," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202305, University of Kansas, Department of Economics, revised Aug 2023.
    12. Guido W. Imbens & Davide Viviano, 2023. "Identification and Inference for Synthetic Controls with Confounding," Papers 2312.00955, arXiv.org.
    13. Rong J. B. Zhu, 2023. "Synthetic Regressing Control Method," Papers 2306.02584, arXiv.org, revised Oct 2023.
    14. Sandro Heiniger, 2024. "Data-driven model selection within the matrix completion method for causal panel data models," Papers 2402.01069, arXiv.org.

More information

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Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (1) 2020-01-20. Author is listed

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