• Corpus ID: 238583451

Dyadic Double/Debiased Machine Learning for Analyzing Determinants of Free Trade Agreements

@inproceedings{Chiang2021DyadicDM,
  title={Dyadic Double/Debiased Machine Learning for Analyzing Determinants of Free Trade Agreements},
  author={Harold D. Chiang and Yukun Ma and Joel Rodrigue and Yuya Sasaki},
  year={2021}
}
This paper presents novel methods and theories for estimation and inference about parameters in econometric models using machine learning of nuisance parameters when data are dyadic. We propose a dyadic cross fitting method to remove over-fitting biases under arbitrary dyadic dependence. Together with the use of Neyman orthogonal scores, this novel cross fitting method enables root-n consistent estimation and inference robustly against dyadic dependence. We illustrate an application of our… 

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