Smoothed quantile regression for panel data

By Antonio F. Galvao, Kengo Kato. 2015

147 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bfd44c92f67cf49a868eb4141634568d67ab50f1

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Citations in the corpus

Fixed Effects Estimation of Large-TPanel Data Models (2018, unknown): “Galvao & Kato (2016) derived the asymptotic distribution of the estimator in the same models, including the leading order 1/T bias, for a smoothed version of the quantile regression objective function under sequences for which N and T grow at the same rate.”
Panel data quantile regression with grouped fixed effects (2018, background): “ approach. Another line of work that focuses on estimating common parameters while putting no structure on individual eects includes Kato, Galvao, and Montes-Rojas (2012), Galvao and Wang (2015) and Galvao and Kato (2016). Alternative approaches have also emerged. Abrevaya and Dahl (2008) take a ran”
High‐dimensional quantile regression: Convolution smoothing and concave regularization (2021, background): “Because of the extremely long list of relevant literature, we refer the reader to the monographs Bühlmann and van de Geer (2011), Hastie et al. (2015), Wainwright (2019), Fan et al.”
Smoothing Quantile Regressions (2019, unknown): “Finally, one could also extend our smoothing approach to handle panel QR as in Galv˜ao and Kato (2016).”
Quantile Factor Models (2017, unknown): “Note that the second term, which makes the standard KS test supτ∈T |v̂T (τ)| invalid, is due to the estimation of the unknown parameter λi, an issue which is known in the literature as Durbin’s problem (see Durbin 1973 and Koenker and Xiao 2002). Following Koenker and Xiao (2002), one could easily m”