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Inference on Functionals under First Order Degeneracy

Qihui Chen, Zheng Fang

arXiv 15 Jan 2019 · Econometrics · publishedJournal of Econometrics (2019) · 5 citations (OpenAlex)

arXiv:1901.04861 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper presents a unified second order asymptotic framework for conducting inference on parameters of the form $\phi(\theta_0)$, where $\theta_0$ is unknown but can be estimated by $\hat\theta_n$, and $\phi$ is a known map that admits null first order derivative at $\theta_0$. For a large number of examples in the literature, the second order Delta method reveals a nondegenerate weak limit for the plug-in estimator $\phi(\hat\theta_n)$. We show, however, that the `standard' bootstrap is consistent if and only if the second order derivative $\phi_{\theta_0}”=0$ under regularity conditions, i.e., the standard bootstrap is inconsistent if $\phi_{\theta_0}”\neq 0$, and provides degenerate limits unhelpful for inference otherwise. We thus identify a source of bootstrap failures distinct from that in Fang and Santos (2018) because the problem (of consistently bootstrapping a nondegenerate limit) persists even if $\phi$ is differentiable. We show that the correction procedure in Babu (1984) can be extended to our general setup. Alternatively, a modified bootstrap is proposed when the map is in addition second order nondifferentiable. Both are shown to provide local size control under some conditions. As an illustration, we develop a test of common conditional heteroskedastic (CH) features, a setting with both degeneracy and nondifferentiability -- the latter is because the Jacobian matrix is degenerate at zero and we allow the existence of multiple common CH features.

Citation extraction

67
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distinct cited
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Babu, G. J (1984) Bootstrapping statistics with linear combinations of Chi-squares as weak limit1.00084100%
2Efron, B (1979) Bootstrap methods: Another look at the Jackknife1.00073100%
3Hall, P. and Horowitz, J. L (1996) Bootstrap critical values for tests based on Generalized-Method-of-Moments estimators1.00054100%
4Dümbgen, L (1993) On nondifferentiable functions and the bootstrap1.00053100%
5Andrews, D. W. K. and Soares, G (2010) Inference for parameters defined by moment inequalities using generalized moment selection0.9416483%
6Andrews, D. W. K. and Shi, X (2013) Inference based on conditional moment inequalities0.9416383%
7Dovonon, P. and Renault, E (2013) Testing for common conditionally heteroskedastic factors0.93237681%
8Chernozhukov, V., Hong, H. and Tamer, E (2007) Estimation and confidence regions for parameter sets in econometric models0.9285480%
9Linton, O., Song, K. E. and Whang, Y.-J (2010) An improved bootstrap test of stochastic dominance0.8746367%
10Fang, Z. and Santos, A (2018) Inference on directionally differentiable functions self0.86534965%

Showing the top 10 of 88 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Improved Inference on the Rank of a Matrix0.909124
2Unified Inference on Moment Restrictions with Nuisance Parameters0.675264
3Statistical Inference of Optimal Allocations 1: Regularities and their Implications0.51122
4Uniform inference for value functions0.40511
5Inference under First-Order Degeneracy0.40511