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Naoya Sueishi

Kobe University (from arXiv:2510.10527, 2025) · OpenAlex

16 papers in scope · 13 published · 2 on the econ.EM arXiv · 72 citations · h-index 4 (over the papers listed here)

Related authors

The 20 authors closest to this one in our weighted citation graph, most related first.

  1. Niall Keleher
  2. Xinkun Nie
  3. Timothy B. Armstrong
  4. Anthony Strittmatter
  5. Michael Lechner
  6. Jann Spiess
  7. Michael C. Knaus
  8. Michal Kolesár
  9. Stefan Wager
  10. Stéphane Bonhomme
  11. Susan Athey
  12. Martin Weidner
  13. Ming Guan
  14. Komei Fujita
  15. Guido W. Imbens
  16. Victor Chernozhukov
  17. Bruno Fava
  18. Gabriel Okasa
  19. Vasilis Syrgkanis
  20. Pedro H. C. Sant’Anna

Proximity is measured over citations between two papers we both hold, weighted by how heavily one leans on the other, and is symmetric — it does not distinguish citing from being cited. Authors without a profile here are skipped, and a genuinely close colleague can be missing simply because their work is not in our arXiv corpus. Method: docs/06-citations-pipeline.md.

Papers

(3 of 16)

Denoised IPW-Lasso for Heterogeneous Treatment Effect Estimation in Randomized Experiments
working paper2025 · arXiv
with Ming Guan, Komei Fujita, Shota Yasui
Paper profile arXiv arXiv:2510.10527
Small Tuning Parameter Selection for the Debiased Lasso
published2024 · Journal of Business and Economic Statistics · first circulated 2022
with Akira Shinkyu
Journal of Business and Economic Statistics
LARGE SAMPLE JUSTIFICATIONS FOR THE BAYESIAN EMPIRICAL LIKELIHOOD
published2022 · Econometric Theory · first circulated 2020
Econometric Theory
On the Informativeness of Specification Tests for Estimator Validity
working paper2022 · arXiv
Paper profile arXiv arXiv:2211.11915
A Misuse of Specification Tests
working paper2022 · arXiv
Paper profile arXiv arXiv:2211.11915
Forecasting the Japanese macroeconomy using high-dimensional data
published2020 · Japanese Economic Review · 4 citations · first circulated 2019
with Yoshiki Nakajima
Japanese Economic Review
On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator
published2019 · Econometrics · 2 citations · first circulated 2017
with Tomohiro Ando
Econometrics
Regularization parameter selection for penalized empirical likelihood estimator
published2019 · Economics Letters · 1 citations · first circulated 2018
with Tomohiro Ando
Economics Letters
Are pension types associated with happiness in Japanese older people?: JAGES cross-sectional study
published2018 · PLoS ONE · 33 citations
with Ichiro Sasaki, Katsunori Kondo, Naoki Kondo, Jun Aida, Hiroshi Ichikawa, Takashi Kusumi, Yuichi Imanaka
PLoS ONE
Focused Information Criterion for Series Estimation in Partially Linear Models
published2017 · Japanese Economic Review · first circulated 2014
with Arihiro Yoshimura
Japanese Economic Review
A NOTE ON GENERALIZED EMPIRICAL LIKELIHOOD ESTIMATION OF SEMIPARAMETRIC CONDITIONAL MOMENT RESTRICTION MODELS
published2016 · Econometric Theory · 3 citations
Econometric Theory
A simple derivation of the efficiency bound for conditional moment restriction models
published2015 · Economics Letters · 4 citations
Economics Letters
Generalized Empirical Likelihood-Based Focused Information Criterion and Model Averaging
published2013 · Econometrics · 13 citations
Econometrics
Identification problem of the exponential tilting estimator under misspecification
published2013 · Economics Letters · 8 citations
Economics Letters
Efficient Estimation and Model Selection for Grouped Data with Local Moments
published2008 · JOURNAL OF THE JAPAN STATISTICAL SOCIETY · 3 citations
with Kohtaro Hitomi, Qingfeng Liu, Yoshihiko Nishiyama
JOURNAL OF THE JAPAN STATISTICAL SOCIETY
Efficiency Improvement by Local Moments in Grouped Data Analysis
published2006 · Medical Entomology and Zoology · 1 citations
with Qingfeng Liu, Kohtaro Hitomi, Yoshihiko Nishiyama
Medical Entomology and Zoology

Assembled from arXiv and OpenAlex. Duplicate records for the same paper are merged, and the published version is shown where we could identify one. Corrections welcome.