Research Fellow (Econometrics and Machine Learning)
Quick Summary
Develop econometric theory for weak-identification-robust tests, bootstrap methods, estimator combinations, and leave-cluster-out bias correction and inference methods.
PhD in Economics, Econometrics, Statistics, or a closely related field by the appointment start date. Strong econometric theory, probability, statistics, and research skills in IV methods,
The Economics Programme in NTU’s School of Social Sciences seeks a Research Fellow. The research develops reliable instrumental variable (IV) estimation/inference method and machine learning algorithm with weak instruments, high-dimensional instruments or controls, and clustered data.
Responsibilities
~1 min read- →Develop econometric theory for weak-identification-robust tests, bootstrap methods, estimator combinations, and leave-cluster-out bias correction and inference methods.
- →Implement simulations and empirical analyses of import competition, education, and pretrial decisions using IV regressions.
- →Prepare reproducible code, research papers, technical appendices, and presentations in collaboration with the project team.
Requirements
~1 min read- PhD in Economics, Econometrics, Statistics, or a closely related field by the appointment start date.
- Strong econometric theory, probability, statistics, and research skills in IV methods, causal inference, and machine learning.
- Proficiency in R, Python, MATLAB, or Julia; ability to implement simulations and analyse data.
- Strong communication and collaboration skills; specialist experience in weak identification, bootstrap methods, or cluster-robust inference is desirable.
- Entry level PhD candidates who meet the qualification requirement by the start date are welcome; prior postdoctoral experience is desirable.
We regret to inform that only shortlisted candidates will be notified.
Hiring Institution: NTULocation & Eligibility
About this job
- Who can apply
- Singapore
- Posted
- 13 days ago
Listing Details
- Posted
- October 5, 2026
- First seen
- October 18, 2026
- Last seen
- October 18, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 17%
- Scored at
- October 18, 2026
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