Massachusetts Institute of Technology Department of Economics Working Paper Series Conditional Quantile Processes Based on Series or Many Regressors Conditional Quantile Processes Based on Series or…

By Alexandre Belloni, Victor Chernozhukov, Iván Fernándex-Val, Iv´an Fern´andez-Val, Andrew Chesher, Roger W. Koenker, Oliver B. Linton, T. Komarova

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Citations in the corpus

Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, methodology): “We use data from the 1994-1996 Canadian National Private Vehicle Use Survey (Semenova and Chernozhukov (2021b)), using sample selection and variable construction as in Yatchew and No (2001) and Belloni et al. (2019).”
Quantile Selection Models with an Application to Understanding Changes in Wage Inequality (2017, unknown): “Evaluating (6) at (z1, z2) = (z, z̃), and using (7) to bound Gx (τ , p(z̃)), we obtain the following bounds on Gx (τ , p(z)): Gx (τ , p(z)) ≤ inf z̃∈Zx FY |D=1,Z [ F Y |D=1,Z ( min ( τ p(z̃) , 1 ) ∣∣ z̃ ) ∣∣ z ] (8)”
Some new asymptotic theory for least squares series: Pointwise and uniform results (2012, background): “The only other reference that provides analogous results but for quantile series estimator is Belloni et al. (2011). Before doing uniform analysis, we also update the pointwise results of Newey (1997) to weaker, more general conditions.”
Gaussian approximation of suprema of empirical processes (2012, methodology): “, ψK,K defined on [0, 1]d.”
Conditional quantile processes based on series or many regressors (2011, background): “After the first version of this paper appeared in [8], Chao, Volgushev and Cheng [19] developed related results for QR-series coefficient processes based on weak approximations.”
Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models (2013, background): “This was proven theoretically in [18] and in [4] under the p→ ∞, p3/n→ 0 regime.”