Subsampling Inference on Quantile Regression Processes

By Victor Chernozhukov, Iv'an Fern'andez-Val. 2005

103 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/101f39dbc95f8a915ce3f2eebfd7bb1a8d18e951

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Citations in the corpus

Program evaluation and causal inference with high-dimensional data (2013, unknown): “…large sample distribution: √ n(∆̂∗ − ∆̂) B φ′ρ(ZP ), uniformly in P ∈ Pn. (3.25) These results can be used to construct simultaneous confidence bands and test functional hypotheses on ∆ using the methods described for example in Chernozhukov and Fernández-Val (2005) and Chernozhukov et al. (2013).”
Program evaluation with high-dimensional data (2013, unknown): “…large sample distribution: √ n(∆̂∗ − ∆̂) B φ′ρ(ZP ), uniformly in P ∈ Pn. (3.25) These results can be used to construct simultaneous confidence bands and test functional hypotheses on ∆ using the methods described for example in Chernozhukov and Fernández-Val (2005) and Chernozhukov et al. (2013).”
Inference on counterfactual distributions (2009, methodology): “…that the estimators x 7→ F̂Xk(x), k ∈ {0, 1}, of the covariate distribution functions are well behaved, specifically that they converge jointly in law to 8Exceptions include Chernozhukov and Hansen (2006) and Chernozhukov and Fernandez-Val (2005), but they looked at forms of subsampling only.”
Testing for Granger-causality in quantiles (2018, background): “Subsampling is a resamplingmethod that provides asymptotic inference for general quantile regressionmodels in a time series context, see Politis et al. (1999) and Chernozhukov and Fernández-Val (2005).”
Measuring and testing for the systemically important financial institutions (2012, methodology): “25A stronger form of consistency of the conditional quantile function requires more stringent regularity conditionals and it is explored in Basset and Koenker (1982) 26Such hypothesis test is also known as the constant effect hypothesis, see Chernozhukov and Fernandez-Val (2005)”
Nonparametric estimation and inference on conditional quantile processes (2015, methodology): “Chernozhukov and Fernández-Val (2005) considered the same issue and suggested resampling as an alternative approach. Angrist, Chernozhukov and Fernández-Val (2006) established inferential theory in misspecied models.”
Extremal quantile regression: An overview (2016, methodology): “, S}, compute Z∗ b,s(kT ) or Ẑ∗ b,s(kT ) applying (4)Chernozhukov and Fernández-Val (2005) suggest the choice b = ⌊m + T ⌋ with c ≥ 2 and m > 0, to guarantee that the minimal subsample size is m.”
QUANTILE AND PROBABILITY CURVES WITHOUT CROSSING (2007, methodology): “Our results also apply to many other econometric problems with monotonicity restrictions, such as demand and production functions, option pricing functions, yield curves, distribution functions, and structural quantile functions (see Matzkin, 1994, for more examples and additional references).”
Quantile Selection Models with an Application to Understanding Changes in Wage Inequality (2017, unknown): “[20] Chernozhukov, V.”