Adaptive test of conditional moment inequalities

By D. Chetverikov. 2011

61 citations recorded by the source corpus.

In this paper, the author constructs a new test of conditional moment inequalities based on studentised kernel estimates of moment functions. The test automatically adapts to the unknown smoothness of the moment functions, has uniformly correct asymptotic size, and is rate optimal against certain classes of alternatives. Some existing tests have nontrivial n-½-local alternatives of the certain type whereas my method only allows (n / log n)-½ - local alternatives of this type. There exist, however, large classes of sequences of well-behaved alternatives against which the test developed in this paper is consistent and those tests are not.

Subfields: Economics and Econometrics, Management Science and Operations Research, Marketing

View Paper: https://www.semanticscholar.org/paper/69e8deb6dc3de16784639dc79cadfa76ac7bbc31

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Citations in the corpus

Testing Many Moment Inequalities (2013, methodology): “Indeed, by Theorem 2 in Chernozhukov, Chetverikov, and Kato (2013b), the primary factor for the bound on the Kolmogorov distance between the conditional distribution of W and the distribution of max1≤j≤p Yj is”
Weighted KS Statistics for Inference on Conditional Moment Inequalities (2011, background): “on the identified set considered in this paper, a few papers written after or around the same time the first draft of the present paper circulated have made important contributions in proposing less conservative critical values for the related problem of inference on points in the identified set (se”
Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors (2012, background): “[29] and [33], and so the limitation is rather major, and was the principal motivation for our whole paper.”
Inference on Causal and Structural Parameters using Many Moment Inequalities (2013, methodology): “Indeed, by Theorem 2 in Chernozhukov, Chetverikov, and Kato (2013b), the primary factor for the bound on the Kolmogorov distance between the conditional distribution of W and the distribution of max1≤j≤p Yj is”
Asymptotically exact inference in conditional moment inequality models (2011, result): “The results in this paper relate to recent work on testing conditional moment inequalities, including papers by Andrews and Shi (2013), Kim (2008), Khan and Tamer (2009), Chernozhukov, Lee, and Rosen (2009), Lee, Song, and Whang (2011), Ponomareva (2010), Menzel (2008) and Armstrong (2011).”
TESTING REGRESSION MONOTONICITY IN ECONOMETRIC MODELS (2012, methodology): “me subsets of the support of X. The rst selection procedure, which is based on the one-step approach, is related to those developed in Chernozhukov, Lee, and Rosen (2013), Andrews and Shi (2010), and Chetverikov (2012), all of which deal with the problem of testing conditional moment inequalities. T”
Nonparametric Inference Based on Conditional Moment Inequalities (2014, unknown): “As far as we are aware, the only other paper in the literature that covers the examples described above is Chernozhukov, Lee, and Rosen (2008) (CLR).”
Improved central limit theorem and bootstrap approximations in high dimensions (2019, unknown): “rm (2) allows us to develop powerful inference methods for high-dimensional data in applications discussed above and has stimulated further developments into dependent data [36, 35, 15], U-statistics [17, 9, 10], Malliavin calculus [18], and homogeneous sums [25]. Despite such rapid developments, th”