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Quantile-Regression Inference With Adaptive Control of Size

Juan Carlos Escanciano, Chuan Goh

arXiv 18 Jul 2018 · Econometrics

arXiv:1807.06977 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Regression quantiles have asymptotic variances that depend on the conditional densities of the response variable given regressors. This paper develops a new estimate of the asymptotic variance of regression quantiles that leads any resulting Wald-type test or confidence region to behave as well in large samples as its infeasible counterpart in which the true conditional response densities are embedded. We give explicit guidance on implementing the new variance estimator to control adaptively the size of any resulting Wald-type test. Monte Carlo evidence indicates the potential of our approach to deliver powerful tests of heterogeneity of quantile treatment effects in covariates with good size performance over different quantile levels, data-generating processes and sample sizes. We also include an empirical example. Supplementary material is available online.

Citation extraction

35
references
64
in-text mentions
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distinct cited
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8,144
main-text words

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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
1Hendricks, W. and R. Koenker (1992) Hierarchical spline models for conditional quantiles and the demand for electricity1.00053100%
2Powell, J. L (1991) Estimation of monotonic regression models under quantile restrictions1.00053100%
3Portnoy, S (2012) Nearly root-$n$ approximation for regression quantile processes0.87462100%
4Koenker, R (2018) quantreg: Quantile Regression0.73732100%
5Koenker, R. and Z. Xiao (2002) Inference on the quantile regression process0.69361100%
6Koenker, R (2005) Quantile Regression0.64422100%
7Koenker, R. and G. Bassett (1978) Regression quantiles0.64422100%
8Newey, W. K. and J. L. Powell (1990) Efficient estimation of linear and type I censored regression models under conditional quantile restrictions0.64422100%
9R Core Team (2016) R: A Language and Environment for Statistical Computing0.64422100%
10Hall, P. and S. J. Sheather (1988) On the distribution of a Studentized quantile0.58531100%

Showing the top 10 of 35 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
1Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates0.40511