Testing Many Moment Inequalities

By Victor Chernozhukov, D. Chetverikov, Kengo Kato. 2013

74 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Marketing, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/27bf289c9d28c2c314ecaacec12b4bf6607e7a78

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Citations in the corpus

Orthogonal Machine Learning for Demand Estimation: High Dimensional Causal Inference in Dynamic Panels (2017, background): “As in [Chernozhukov et al., 2013a], the Gaussian approximation results above could be used not only for simultaneous confidence bands but also for multiple hypothesis testing using the step-down methods.”
Cross-Validation With Confidence (2017, methodology): “Our test uses some recent results in high-dimensional Gaussian comparison developed by [7, 6], which allows us to provide theoretical guarantees for our procedure under weak assumptions.”
Performance bounds for parameter estimates of high-dimensional linear models with correlated errors (2016, background): “By Lemma A.3 in [10], there exists an absolute constant K such that [10], there exists an absolute constant K 1 > 0 and a constant K q only depending on q such that To deal with max i ≤ p − s | ζ i | , we have similarly the moment inequality”
High-dimensional econometrics and regularized GMM (2018, background): “Also, [46] used such results to develop computationally efficient tests of many moment inequalities for heavy-tailed data.”
Breaking the curse of dimensionality in regression (2017, methodology): “We follow Candes and Tao (2007) and choose the tuning parameters ηθ and ηγ as the empirical maximum of |Xξ|i over several realizations of ξ ∼ N (0, In); alternatively, one can choose these tuning parameters as in Section 4 of Chernozhukov et al. (2013a).”
Gaussian Approximation for High Dimensional Time Series (2015, background): “Zhang and Cheng (2014) extended the Gaussian approximation in Chernozhukov et al. (2013a) to very weakly dependent random vectors which satisfy a uniform geometric moment contraction condition.”
Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels. (2017, methodology): “Since S̃il are independent across i and l, we may apply Corollary 2.1 in Chernozhukov et al. (2013).”