Weighted KS statistics for inference on conditional moment inequalities

Armstrong, Timothy B.

doi:10.1016/j.jeconom.2014.04.021

Cited by 85 publications

(92 citation statements)

References 35 publications

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“…First, our paper does not cover conditional moment restrictions (cf. Shi (2013), Chernozhukov, Lee, andRosen (2013), Armstrong (2014), andChetverikov (2013)). Second, alternative asymptotic frameworks like those in Andrews and Barwick (2012) and Romano, Shaikh, and Wolf (2014) may provide a better asymptotic approximation for the type of problems we study in this paper.…”

Section: Discussionmentioning

confidence: 99%

Inference for subvectors and other functions of partially identified parameters in moment inequality models

Bugni

Canay²,

Shi

2017

Quantitative Economics

125

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This paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in)equality model. These functions are restricted to be linear for two-sided testing problems, but may be nonlinear for one-sided testing problems. In the most common case, this function selects a subvector of the parameter, such as a single component. The new inference method we propose controls asymptotic size uniformly over a large class of data distributions and improves upon the two existing methods that deliver uniform size control for this type of problem: projection-based and subsampling inference. Relative to projection-based procedures, our method presents three advantages: (i) it weakly dominates in terms of finite sample power, (ii) it strictly dominates in terms of asymptotic power, and (iii) it is typically less computationally demanding. Relative to subsampling, our method presents two advantages: (i) it strictly dominates in terms of asymptotic power (for reasonable choices of subsample size), and (ii) it appears to be less sensitive to the choice of its tuning parameter than subsampling is to the choice of subsample size.

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Section: Discussionmentioning

confidence: 99%

Inference for subvectors and other functions of partially identified parameters in moment inequality models

Bugni

Canay²,

Shi

2017

Quantitative Economics

125

View full text Add to dashboard Cite

show abstract

“…First, our paper does not consider conditional moment restrictions, c.f. Andrews and Shi (2013), Chernozhukov et al (2013), Armstrong (2014), andChetverikov (2013). Second, our asymptotic framework is one where the limit distributions do not depend on tuning parameters used at the moment selection stage, as opposed to Andrews and Barwick (2012) and Romano et al (2014).…”

Section: Discussionmentioning

confidence: 99%

Inference for functions of partially identified parameters in moment inequality models

Bugni¹,

Canay²,

Shi³

2014

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThis paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in)equality model. As a special case, our method yields marginal confidence sets for individual coordinates of this parameter vector. Our inference method controls asymptotic size uniformly over a large class of data distributions. The current literature describes only two other procedures that deliver uniform size control for this type of problem: projection-based and subsampling inference. Relative to projection-based procedures, our method presents three advantages: (i) it weakly dominates in terms of finite sample power, (ii) it strictly dominates in terms of asymptotic power, and (iii) it is typically less computationally demanding. Relative to subsampling, our method presents two advantages: (i) it strictly dominates in terms of asymptotic power (for reasonable choices of subsample size), and (ii) it appears to be less sensitive to the choice of its tuning parameter than subsampling is to the choice of subsample size.

show abstract

“…First, our paper does not consider conditional moment restrictions, c.f. Andrews and Shi (2013), Chernozhukov et al (2013), Armstrong (2011), andChetverikov (2013). Second, our asymptotic framework is one where the limit distributions do not depend on tuning parameters used at the moment selection stage, as opposed to Andrews and Barwick (2012) and Romano et al (2013).…”

Section: Discussionmentioning

confidence: 99%

Inference for functions of partially identified parameters in moment inequality models

Bugni¹,

Canay²,

Shi³

2014

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract This paper introduces a new hypothesis test for the null hypothesis H0 : f (θ) = γ0, where f (·) is a known function, γ0 is a known constant, and θ is a parameter that is partially identified by a moment (in)equality model. The main application of our test is sub-vector inference in moment inequality models, that is, for a multidimensional θ, the function f (θ) = θ k selects the kth coordinate of θ. Our test controls asymptotic size uniformly over a large class of distributions of the data and has better asymptotic power properties than currently available methods. In particular, we show that the new test has asymptotic power that dominates the one corresponding to two existing competitors in the literature: subsampling and projection-based tests. Terms of use: Documents in

show abstract

Weighted KS statistics for inference on conditional moment inequalities

Cited by 85 publications

References 35 publications

Inference for subvectors and other functions of partially identified parameters in moment inequality models

Inference for subvectors and other functions of partially identified parameters in moment inequality models

Inference for functions of partially identified parameters in moment inequality models

Inference for functions of partially identified parameters in moment inequality models

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