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- W3004686042 abstract "Summary This paper provides an orthogonal extension of the semiparametric difference-in-differences estimator proposed in earlier literature. The proposed estimator enjoys the so-called Neyman orthogonality (Chernozhukov et al., 2018), and thus it allows researchers to flexibly use a rich set of machine learning methods in the first-step estimation. It is particularly useful when researchers confront a high-dimensional data set in which the number of potential control variables is larger than the sample size and the conventional nonparametric estimation methods, such as kernel and sieve estimators, do not apply. I apply this orthogonal difference-in-differences estimator to evaluate the effect of tariff reduction on corruption. The empirical results show that tariff reduction decreases corruption in large magnitude." @default.
- W3004686042 created "2020-02-14" @default.
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- W3004686042 date "2020-02-04" @default.
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- W3004686042 title "Double/debiased machine learning for difference-in-differences models" @default.
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- W3004686042 doi "https://doi.org/10.1093/ectj/utaa001" @default.
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